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Deep ensemble model for skin cancer classification with improved feature set

Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Mindbrush: A Machine Learning Approach to Virtual Artistry 2

Mindbrush: A Machine Learning Approach to Virtual Artistry

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Kashyap, Shreya Kaviya, A. Yamini, R. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technologies Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350368109

Air Canvas is a breakthrough virtual platform of digital artistry which is able to cross the boundaries of traditional art by allowing users to create their artusing only gestures in the air. The novel technique of computer vision, particularly the hand tracking and finger-tip recognition, has been incorporated and it has provided a completely new definition of HCI (human-computer interaction). Through the absence of the physical tools like pens or keyboards, Air Canvas delivers a liberated experience that helps people develop and communicate in a way that is natural, and simpler. This amazing concept is one more step toward widening accessibility and making sure that people like the deaf, aged, and those with physical disabilities are not left behind. © 2024 IEEE.

关键词： Audition

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Role and Challenges of Emerging Technologies in Healthcare Application 6th

Role and Challenges of Emerging Technologies in Healthcare A...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Vedanayagam, Sumathy Cyriloose, Pretty Diana Cyril Perumal, Govindaraj Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology TN Kattankulathur India Department of Biomedical Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819780891

In the healthcare domain revolution, emerging technologies provide innovative solutions to enhance patient care and medical research. Each new concept has potential for specific problems, and they also have some challenges. Many technologies are introduced to improve service with security and qualities in healthcare applications like Blockchain, Digital Healthcare Systems, Nano technology, Augmented Reality, Virtual Reality, and Artificial Intelligence. Blockchain technology yields security and transparency for electronic health records. We elaborate on the benefits and challenges of blockchain in healthcare applications. Powerful technology can be applied to various healthcare domains for the betterment and contribution of innovative solutions to healthcare. Blockchain has multiple features, including distributed ledger, decentralized storage, authentication, security, and immutability, which greatly solve complex healthcare problems. Blockchain plays an essential role in healthcare applications such as preserving and sharing patient data, diagnostic labs, and pharmacy stores to effectively handle narcotic and other prescribed drugs for safe use by clinicians, handling the patient medical records and clinical trial data. technology also improves the security of patient medical records and the efficient operation of healthcare organizations. One of the recent applications of blockchain in the healthcare system is customized function to personalized medicine in the health information technology by providing shareable private keys by the healthcare providers. In addition, blockchain technology can maintain patients’ health information confidentially, which improves remote access to the individual. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Utilizing Big Data Analytics for Social Media Insights 5

Utilizing Big Data Analytics for Social Media Insights

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vedhika Srinidhi, V.S. Sanjana, S. Rozario, Neo Joseph Dewan, Sia Gouthaman, P. School of Computing College of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

Big Data analytics is a rapidly evolving field that offers profound insights into various sectors by enabling the analysis of massive and diverse datasets. This research paper focuses on the application of Big Data techniques to social media and airline networks, leveraging tools such as sentiment analysis, clustering, and data visualization to extract actionable information. The study analyses Twitter data to uncover public sentiment trends and classifies topics using advanced machine learning techniques. Furthermore, the research explores the use of clustering algorithms within the Hadoop ecosystem to process large-scale datasets efficiently. Insights from airline customer feedback and network planning data demonstrate how Big Data analytics can optimize operational efficiency and enhance customer satisfaction. Key findings include the identification of distinct sentiment patterns in social media data, the efficiency of novel clustering techniques in handling high-volume datasets, and the utility of advanced visualization methods to convey complex data. These results have significant implications for industries ranging from social media platforms to the airline sector, providing valuable tools for enhancing decision-making processes and improving user experiences across diverse domains. The study highlights the potential of Big Data analytics to drive innovation and operational excellence in dynamic environments. © 2024 IEEE.

关键词： Airline networks Big Data analytics Clustering algorithms Data visualization Sentiment analysis Social media

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Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model

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Environmental Science and Pollution Research 2024年第59期31卷 66780-66795页

作者： Shad, Mohammad Sharma, Yogeshver Dutt Narula, Pankaj Department of Mathematics and Scientific Computing National Institute of Technology Himachal Pradesh Hamirpur177005 India School of Mathematics Thapar Institute of Engineering & Technology Punjab Patiala147004 India

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India’s five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike’s information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and βSARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04–0.12), MAE (0.03–0.1), SMAPE (0.04–0.2), and MASE (0.5–.08). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Indoor air pollution

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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Supporting V2V communications-based Internet of Drones 21

Supporting V2V communications-based Internet of Drones

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Daoud, Omar R. Hamarsheh, Qadri J. Darnati, Ahlam A. Qasem, O. Al-Khawaldah, M. School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Faculty of Engineering and Technology Philadelphia University Electrical Engineering Department Amman Jordan

ISBN: (纸本)9798350374131

The effective customization of service provisioning in the latest communication generation hinges on addressing critical concerns related to Quality of Service (QoS) and radio resources. The advent of new technologies, particularly within the realm of the Internet of Things (IoT), provides a backbone for supporting service provisioning. This paper investigates the utilization of the Internet of Drones (IoD) as mobile base stations to enhance connectivity in Vehicle-to-Vehicle (V2V) communications. The proposal aims to adapt a previously published V2V benchmark, introducing modifications to QoS by incorporating the multi-parallel processing stage of Daubechies wavelets and assessing the Bit Error Rate (BER). The efficacy of the proposed approach is compared against both conventional communication links and benchmark results from wavelet-based studies. Simulation outcomes substantiate the effectiveness of the proposed scheme, particularly in terms of reducing power peaks and improving BER performance indicators. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Real-Time Weapon Detection System for Enhanced Security Monitoring 4

Real-Time Weapon Detection System for Enhanced Security Moni...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kuppili, Rohan Agarwal, Khusboo Bhosale, Tanmay Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Innovative technology for weapon detection during security monitoring is necessary today due to the rising threats of violence and terror. The paper discusses a detailed design of a weapon detection system intended for application in enhancing security surveillance specifically using a deep learning approach. The system employs YOLOv3 (You Only Look Once, Version 3) mood and adaption aimed at live footage weapon detection. To do this on broader scale, this system incorporates big data tools Hadoop, Docker, Spark and Kafka for reasons of real time processing and effectiveness in high volume data situations. This model is applied on the live footage surveillance of public and private places to determine if the model can detect weapons, to warn relevant authorities in the aim of decreasing response time and averting security breach occurrences. The experiments conducted confirm that the model is capable of performing weapon detection tasks in an indoor environment with changes in illumination conditions with few false alarms. © 2024 IEEE.

关键词： Big data

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Active Voltage Balancing of Series-Connected SiC MOSFETs During Body-Diode Turn-Off Using an Active Gate Driver

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IEEE Transactions on Power Electronics 2025年第8期40卷 10609-10622页

作者： Rai, Ajay Ganesan, P. Hatua, Kamalesh Bhattacharya, Subhashish Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram India Department of Electrical and Computer Engineering NC State University Raleigh NC USA

The series connection of fast-switching SiC MOSFETs presents a promising approach to enhance the efficiency, compactness, and simplicity of Medium Voltage (MV) power converters. However, managing voltage imbalances during the turn-off of MOSFETs and their body diodes introduces significant challenges. While existing literature covers passive and active voltage balancing strategies for switch turn-off (e.g., MOSFETs/IGBTs), the issue of uneven voltage distribution during body diode turn-off remains largely unexplored. This gap limits the widespread adoption of series-connected devices in MV power converters, as achieving reliable performance with minimal additional components and low switching losses remains difficult. This article presents an Active Voltage Balancing technique for handling body diode turn-off events by regulating the gate charge of parallel MOSFETs during voltage rise transients in the closedloop using Active Gate Drivers (AGD). This approach ensures uniform voltage distribution among the body diodes of seriesconnected MOSFETs. A comprehensive analysis and modelling of gate charge control during voltage transients is presented. The proposed method is experimentally validated on two seriesconnected SiC MOSFETs operating in buck mode (DC-DC) and half-bridge inverter mode (DC-AC) at a 1.5 kV DC bus voltage, with switching frequencies up to 40 kHz. © 1986-2012 IEEE.

关键词： Diodes

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Financial Fraud Detection for Credit Card Transactions Using Apache Spark 5

Financial Fraud Detection for Credit Card Transactions Using...

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

作者： Jha, Pratyush Srivastava, Srijan Gandhi, Tanishq Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The financial sector's rapid adoption of digital transformation has made identifying fraud a progressively complex challenge, since modern fraudulent activities present significant risks to institutions globally. This paper presents a novel methodology for detecting financial fraud that integrates big data analytics and machine learning techniques within a distributed environment. With an extensive number of actual financial transactions, the system uses Apache Spark for real-time data processing and machine learning algorithms to accurately classify fraudulent transactions. This method effectively addresses two major issues: efficiently processing large data sets and managing the severe class imbalance between fraudulent and non-fraudulent transactions. Collectively, these solutions provide a scalable and resilient framework for improving fraud detection in the digital era. © 2024 IEEE.

关键词： Nearest neighbor search

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