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An Efficient Deep Transfer Learning Approach for Classification of Skin Cancer Images 7th

An Efficient Deep Transfer Learning Approach for Classificat...

引用

7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Naik, Prithviraj Purushottam Annappa, B. Dodia, Shubham Department of Computer Science and Engineering National Institute of Technology Surathkal Karnataka India

ISBN: (纸本)9783031314063

Prolonged exposure to the sun for an extended period can likely cause skin cancer, which is an abnormal proliferation of skin cells. The early detection of this illness necessitates the classification of der-matoscopic images, making it an enticing study problem. Deep learning is playing a crucial role in efficient dermoscopic analysis. Modified version of MobileNetV2 is proposed for the classification of skin cancer images in seven classes. The proposed deep learning model employs transfer learning and various data augmentation techniques to more accurately classify skin lesions compared to existing models. To improve the per¬formance of the classifier, data augmentation techniques are performed on "HAM10000" (Human Against Machine) dataset to classify seven dif¬ferent kinds of skin cancer. The proposed model obtained a training accuracy of 96.56% and testing accuracy of 93.11%. Also, it has a lower number of parameters in comparison to existing methods. The aim of the study is to aid dermatologists in the clinic to make more accurate diagnoses of skin lesions and in the early detection of skin cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Privacy-Preserving of Edge Intelligence using Homomorphic Encryption 3

Privacy-Preserving of Edge Intelligence using Homomorphic En...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Jerang, Ronaldo Nayak, Sumitra Mahato, Ganesh Kumar Kumar Chakraborty, Swarnendu National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350338607

Edge intelligence has paved the way for a stronger and more secure system by combining security and artificial intelligence. A lot of research have been undertaken, revealing remarkable development and a variety of viewpoints on this novel phenomenon. We suggested a way enabling edge intelligence to move via homomorphic encryption-enabled gadgets and automobiles. This encrypted data will be delivered to an edge node for the building of a machine learning model in order to gather any potential information hidden in the data as well as any potential obstacles that may need to be addressed before any event happens. Several machine learning models, such as KNN, K-means, SVM, and others, are used to gain the best possible data analysis. Once secure, decisions will be taken, and the outcome will be visible. All machine learning training will be done in encrypted data, which will be encrypted to ensure that no one's privacy is abused. This may be set up for a variety of machine learning modules. © 2023 IEEE.

关键词： Deep learning

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A Novel Approach on Deep Reinforcement Learning for Improved Throughput in Power-Restricted IoT Networks 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Sweety Bakyarani, E. Singh, Navneet Pratap Shekhawat, Jyoti Bhardwaj, Saurabh Chaku, Shweta Singh, Jagendra Department of Computer Science SRM Institute of Science and Technology Kattankulathur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Vivekanand Global University Rajasthan Jaipur India Department of Information Technology Raj Kumar Goel Institute of Technology AKTU Lucknow Ghaziabad India Department of Computer Science Engineering Inderprastha Engineering College AKTU Lucknow Ghaziabad India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The rapid expansion of the Internet of Things (IoT) has stressed the importance of energy-efficient communication protocols, particularly in networks operating under power constraints. This paper presents a unique approach for managing communication in energy-limited IoT networks using a deep reinforcement learning (DRL)-based communication protocol. By integrating Sparse Code Multiple Access (SCMA), Code Division Multiple Access (CDMA) techniques, and the Combined Experience Replay Deep Deterministic Policy Gradient (CER-DDPG) algorithm, we developed a novel protocol to improve the throughput of power-constrained sensors in an IoT network. Through comprehensive simulations, we compared the proposed protocol’s performance with benchmark systems like traditional DDPG and stochastic algorithms. The results reveal superior energy efficiency and throughput with the proposed protocol, establishing its potential to significantly enhance the performance of energy-constrained IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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A Review of Machine Learning Techniques for Tuberculosis Meningitis Diagnosis 4th

A Review of Machine Learning Techniques for Tuberculosis Men...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ramteke, Monali Raut, Shital Kadam, Tejal Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789819920990

TB has been considered to be a major global health hazard. With regard to early meningitis tuberculosis identification, numerous studies and research have been conducted in recent years. The most serious form of tuberculosis is tuberculous meningitis (TBM), although it can be challenging to distinguish TBM from other meningitis. Therefore, we reviewed papers related to machine learning models for identification and diagnosis of different forms of meningitis. In this review, the most recent research and methods for integrating medical data with machine learning models for diagnosis of meningitis and its respective outcomes are summarized. To achieve the best and most accurate results for detection and classification, authors have used a variety of strategies, beginning with preprocessing the data and partitioning the dataset into distinct training, validation, and test sets with varied ratios. This brings to attention that various machine learning tools and algorithms are useful in the analysis of medical data to diagnose meningitis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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An Efficient and Secure Cluster-Based Cooperative Data Transmission for Wireless Ad Hoc Networks IOT Environment

An Efficient and Secure Cluster-Based Cooperative Data Trans...

引用

International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Mahapatra, Surya Narayan Singh, Binod Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur831014 India

ISBN: (纸本)9789819901883

In current scenario, we have multiple technologies are available to transmit the data in wireless ad hoc network (WANET).But, compare to other existing models, we have proposed a new model where not only this model helps to reduce the energy consumption of nodes while transmitting the data but also our model focus on security aspect where it will help to solve some of the common attacks such as collusion attack, Dos attack, etc., our model consist of 3 stages/phases, namely as (i) registration, (ii) clustering, and (iii) transmission. In registration phase, each node in the network will identified by unique id, and nodes will registered themselves to the network, and in second phase, nearby nodes will form clustered based on the threshold value and transmitting power. By using multi-hop transmission after finding the route with the help of relay node search optimization (RESO), where appropriate relay node will be selected based on the parameters which will reduce the energy computation by the node and by the blockchain-based transaction from node of one cluster to another cluster. This will deminise the security threat in the network. However, in our model, we expecting the result better than existing models based on the theoretical knowledge. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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LiDAR Sensor for Self-Driving Cars 8

LiDAR Sensor for Self-Driving Cars

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Tummala, Manoj Pravin, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Progressive technological development results in modern automotive technology such as self-driving automobiles. These require sensors to collect data about nearby objects and the surroundings in order to identify lanes and make decisions. Instead of LiDAR sensors (Light Detection and Ranging), Tesla, Waymo, and other successful businesses have employed cameras for developing autonomous vehicles. The disadvantage of camera performance changing with lighting conditions can be solved by using LiDAR. The LiDAR sensor aids in 3D mapping, allowing cars to travel in a predictable environment. The use of cameras for monitoring normal driving circumstances might be difficult because of the large range of features they include, but LiDAR contains infrared light, which is a solution for concerns such as fog, rainy weather, and varied textures. Developing sensors driven by computer vision can lead to more environmental sensing for self-driving automobiles in the future. This study discusses the use of LiDAR sensors in autonomous cars. © 2023 IEEE.

关键词： Autonomous vehicles

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TCI-Net: A Deep Learning Approach for Tropical Cyclone Intensity Prediction

TCI-Net: A Deep Learning Approach for Tropical Cyclone Inten...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Gujral, Sahil Rao, Darshan Khanvilkar, Anuj Shah, Umit Maktum, Tabassum Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350300826

One of the most affecting types of weather phenomena are tropical cyclones (TCs), commonly referred to as typhoons or hurricanes. Atlantic Ocean, North Pacific Ocean, Western Pacific Ocean, South Pacific Ocean, and Indian Ocean are the five oceans where hurricanes, typhoons, and tropical cyclones can form. Many techniques have been tried in the past to monitor cyclones and assess their intensity before they form, but they have always been shown to be ineffective. It is crucial to estimate TC intensity in order to forecast and decrease human loss and suffering. Recently, data scientists in addition to meteorologists have started to show interest in the task. This claim demonstrates an innovative approach based on image processing for predicting TC intensity given a meteorological geostationary satellite imagery. The Convolutional Neural Network (CNN) architecture which has to be modified in a number of ways, will be used to create the suggested cyclone intensity estimating model. The results obtained from experiments conducted on a widely accessible benchmark dataset reveal that the proposed approach stands out as one of the most precise models for estimating TC intensity. Moreover, it exhibits remarkable stability across various scenarios. These findings underscore the substantial promise of harnessing data science in the field of meteorological research. © 2023 IEEE.

关键词： Convolution

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Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals 5

Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Muthukumar, B. Kartheesan, L. Pasupathy, Vijayalakshmi Surendran, R. Rajalakshmi Engineering College Department of Information Technology Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features. © 2024 IEEE.

关键词： Deep learning

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Comparative Analysis of Crash Consistency Techniques in File Systems

Comparative Analysis of Crash Consistency Techniques in File...

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NIELIT's International Conference on Communication, Electronics and Digital technology, NICE-DT 2023

作者： Sondur, Archana Sakhare, Sachin More, Priyanka Department of Computer Engineering Vishwakarma Institute of Information Technology Kondhwa Pune India

ISBN: (纸本)9789819916986

Since it is necessary to manage user data, the file system is an essential part of the operating system. It must be reliable, efficient, and crash proof. For a variety of reasons, file systems are highly likely to experience a system crash or power outage, but with the help of a few crash consistency techniques, the data can still be assured to be crash consistent. To be able to create the required file system, a thorough understanding of these techniques has become highly critical. Hence, the authors have conducted a thorough survey to study crash consistency and its techniques. Using the relevant information that was within their reach, the authors conducted a comparative analysis of the three classic crash consistency techniques which are journaling, log-structuring, and shadow-paging using the factors like consistency achieved, performance, complexity, and availability and presented it in one table, and transaction method, storage, cost required, and main fault in another. With the aid of this study and the analysis, the authors have proposed an adaptive recommendation system to suggest to developers the crash consistency technique most suited for their file system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder 1

An Optimized Hybrid Soil Texture Classification Model using ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Prabavathi, R. Chelliah, Balika J Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques. © 2023 IEEE.

关键词： Deep learning

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