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5th IEEE International Conference on Telecommunications and Photonics, ICTP 2023

作者： Ahmmed, Syed Das, Sanchita Rani Leon, Mazharul Islam Rubaiyat Hossain Mondal, M. Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350393477

The diagnosis of dengue fever can be made quickly and effectively with Raman spectroscopy. However, the diagnostic procedure presents difficulties;for example, the distinction between healthy and infected spectra is extremely subtle. Deep learning (DL)-based diagnosis of dengue from Raman spectroscopy is gaining research interest. This paper introduces an innovative hybrid model that combines the architecture of a long-short-term memory recurrent neural network (LSTM RNN) with the capabilities of a pre-trained convolutional neural network (CNN). We present the CNN-LSTM Transfer Learning (HCL-TL) method, which uses Raman spectroscopy images to transform the field of data-driven dengue disease diagnosis. The proposed approach utilizes hyperparameters to refine the DL models. Both the initial training and subsequent fine-tuning stages incorporate model checkpoints and callbacks to monitor loss and prevent overfitting. A dataset consisting of 635 spectra-460 of which were infected and 175 of which were healthy-was used to evaluate the suggested model. Both holdout and 10-fold cross-validation methods are used for splitting the data samples. When applied to testing data for dengue infection diagnosis, the proposed HCL- TL method achieves a classification accuracy of 97.73%. The HCL-TL method outperforms the state-of-the-art DL and machine learning models in terms of accurately classifying dengue fever from Raman spectroscopy images, according to the results. © 2023 IEEE.

关键词： Raman spectroscopy

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Industrial Systems Optimization from Combining Internet of Things and Cloud Computing

Industrial Systems Optimization from Combining Internet of T...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Nachappa, M.N. Faujdar, Pramod Kumar Agarwal, Parag Department of Computer Science and Information Technology Bangalore India Mechanical Engineering Vivekananda Global University Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Business structures optimization from combining the net of things (IoT) and Cloud Computing is a subject of study that recognizes the vital function that the two technology can play collectively in optimizing industrial methods. IoT leverages numerous statistics resources;the cloud gives a repository where these statistics resources may be stored and maintained, and cloud computing evaluates and optimizes the records to discover potential efficiencies and enhance overall operational performance. This analysis results in becoming aware of and implementing process upgrades and reducing the production fee. As a result, the industrial gadget is in a position to use the statistics generated using the IoT and Cloud Computing to enhance operational efficiency and patron delight. ultimately, the combination of IoT and Cloud Computing resulted in reduced manufacturing fees and improved operational performance. Business structure optimization by combining the Internet of Factors (IoT) and Cloud Computing can bring about stepped forward performance and productivity of organizations and their production approaches. IoT in business settings presents agencies with correct and real-time facts from systems and machines, and cloud computing gives value-effective storage and management of large amounts of such facts. By combining records from one-of-a-kind linked devices, IoT and Cloud Computing may create a 'business Intelligence community,' which may be used to screen and optimize approaches. It permits improved selection-making, elevated production speed, and more advantageous product excellence. Additionally, with the aid of leveraging predictive analytics, the community can be used to assume ability issues in manufacturing techniques and diagnose and prevent them as nicely. This combination of IoT and Cloud Computing can ultimately assist businesses in growing operational efficiency, reducing fees, and gaining an aggressive advantage in the market.. © 2024 IEEE.

关键词： Business efficiency Intelligence resources statistics

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Predictive Modeling for Early Identification of Disease Severity in Acute Respiratory Infections: A Case Study with COVID-19 5

Predictive Modeling for Early Identification of Disease Seve...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kalapuge, Vindhya Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The COVID-19 pandemic, declared a global emergency by WHO in March 2020, has had a profound impact on global health. With millions affected, the pandemic's magnitude became evident. Even advanced healthcare systems grappled with resource shortages, from protective gear to ventilators. Late diagnosis and the inability to adapt to new SARS-CoV-2 variants led to misdiagnoses and high casualties. Overburdened healthcare facilities and the absence of a centralized patient data system hindered swift decision-making. The lack of real-time monitoring meant many didn't receive timely, personalized care, underscoring the need for an integrated healthcare response during global emergencies. To tackle these challenges and improve hospital resource management during pandemics, this study presents a web approach harnessing big data analytics to develop a machine learning predictive model. This model has been developed to enable hospitals to assess patients' severity in real-time, taking into account a wide range of factors such as symptoms, medical history, and demographics. To acquire data regularly, a streaming data pipeline was established using Apache Kafka and Apache Streaming. In predicting the severity levels of patients, both Multinomial Logistic Regression and Multilayer Perceptron (MLP) models were employed. One noteworthy finding was that using datasets with balanced classes yielded significantly higher accuracy for the MLP model compared to unbalanced datasets. Specifically, MLP achieved the highest accuracy of 60.4% when trained on balanced classes. This model's real-time severity insights have the potential to revolutionize decision-making and resource allocation in healthcare. It represents a significant step towards a more effective response to future pandemics and the strengthening of the global healthcare system. © 2023 IEEE.

关键词： COVID-19

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Coordinated Fog-to-Cloud System Managing

Coordinated Fog-to-Cloud System Managing

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

作者： Nair, Amrutha V. Nachappa, M.N. Singh, Vaishali Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Recent developments in cloud services information have sparked several improvements in networking, storage, and computing, among other areas of information technology. The Internet of information, the use of clouds, and the newly developed idea of fog computing have all emerged as a result of this transition. The processing, storing, and exchanging of data between edge devices, user clients, and the cloud are all included in fog computing. Although their quick development was first unanticipated, these technologies currently allow for the implementation of what are known as smart scenarios, such as smart homes, smart cities, and smart transportation. This article outlines the way toward a new IoT environment with highly dispersed and dynamic storage, computing, and networking resources. We also describe an architecture, an illustrated use case, and a comparative productivity study. A wide range of heterogeneity and commodity devices at the edge, developing fog-based resources, and traditional cloud services are all present in this ecosystem. © 2023 IEEE.

关键词： cloud computing decentralized operation disaster management edge devices fog computing Fog-to-Cloud system Internet of Things (IoT) technical challenges and cloud market opportunities urban planning

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Blockchain and Edge Computing for IoT Advancements 3

Blockchain and Edge Computing for IoT Advancements

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jaison, Feon Chaudhary, Chetan Manaswini, R. Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

The rise of cloud computing and distributed ledger technology has enabled the IT sector to implement cutting-edge safeguards for sensitive customer data. This article examines how cloud computing and distributed ledger technologies (DLTs) may be safer due to their applications, advantages, and fundamental principles. It is critical to have a comprehensive awareness of this paradigm's core contributions, driving reasons, research difficulties, and prospective remedies. An approach based on eight mathematical equations and three sophisticated algorithms is also described. The researchers that carried out the previously stated experiment devised this strategy. The first technique, named the 'Secure Data Transmission Algorithm,' involves securely transmitting data from the cloud to DLT. The 'Consensus Protocol Enhancement Algorithm' (CPEA) is a method for accelerating consensus-building in DLT networks. These speeds up the process of improving the security of DLT networks. The 'Privacy-Preserving Smart Contracts Algorithm' is a novel algorithm that has been designed to enable smart contracts while protecting the privacy of its users. The study's findings shed light on the potential benefits of this consolidation and provide a strong foundation for the future development of further preventive measures. The proposed solution, which is supported by complicated mathematical formulations and algorithms, efficiently answers the contemporary information technology era's strong requirement for greater security. This is because the approach relies on sophisticated mathematical formulas as its foundation. © 2023 IEEE.

关键词： Smart contract

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Trusted Computation for Cloud VM Lifetime Security

Trusted Computation for Cloud VM Lifetime Security

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

作者： Aruna, M. Nachappa, M.N. Singh, Akhilendra Pratap Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Virtual machines (VMs) are essential components of cloud computing platforms, but they are subject to many security risks during the course of their existence. Threats in various VM states are often addressed by existing solutions. In order to handle VM security in its entirety, this article proposes a cutting-edge architecture for trusted computing-based VM lifecycle security protection. We build an established computing-based security structure to extend trust across the platform module to a VM, assuring its security and dependability throughout its lifespan. We describe the VM lifecycle using its many active phases. The framework's capacity to guarantee whole VM safety is supported by theoretical analysis, and trials support its viability and higher level of security than existing methods. © 2023 IEEE.

关键词： attestation cloud computing empirical data hardware security security framework security protection security risks Trusted computing trusted status monitoring virtual machines (VMs) VM lifecycle security

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Two-Mode Biomedical Sensor Build-up:Characterization of Optical Amplifier

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computers, Materials & Continua 2022年第3期70卷 5487-5501页

作者： Usman Masud Fathe Jeribi Mohammed Alhameed Faraz Akram Ali Tahir Mohammad Yousaf Naudhani Faculty of Electrical and Electronics Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electrical Communication Engineering University of KasselKassel34127Germany College of Computer Science&Information Technology Jazan UniversityJazan45142Saudi Arabia Faculty of Engineering and Applied Sciences Riphah International UniversityIslamabad44000Pakistan Faculty of Information&Communication Technology Balochistan University of Information TechnologyEngineering and Management SciencesQuetta

Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human *** factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said *** the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their ***,the investigation and technical analysis of the amplifier in the system has been explored in *** algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s *** this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its *** this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.

关键词： Biomedical sensor cavity optomechanics spectroscopy relative intensity noise semiconductor laser diode

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Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

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computers, Materials & Continua 2024年第9期80卷 4749-4765页

作者： Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar Department of Electrical Computer and Telecommunications EngineeringBotswana International University of Science and TechnologyPalapyePrivate Bag 16Botswana College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11586Saudi Arabia

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems *** computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this ***,accurately segmenting the Region of Interest(ROI)fromthermograms remains *** paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree *** proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground *** features were extracted from each view’s ROI,with significant features selected via Mutual information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)*** findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of *** consistency of the method across both sides of the breast suggests its viability as an auto-segmentation ***,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关键词： Breast cancer CAD machine learning ROI segmentation thermography

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Quantum Computing Enhancements in Deep Learning Models for Cybersecurity

Quantum Computing Enhancements in Deep Learning Models for C...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Yadav, Dhyan Chandra Bhagwat, Renuka Saha, Ananya Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

The cybersecurity industry has undergone a sea change due to the combination of quantum computing and deep learning models. This study presents a novel approach that makes use of the unique features of quantum computing to improve the efficiency and safety of deep learning models used in cybersecurity. The suggested method improves upon previous methods in terms of both the precision and efficiency with which threats may be identified and categorized by making use of Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), and Quantum Key Distribution (QKD). Our findings show that the suggested method is superior to conventional approaches through a thorough examination of detection accuracy and computing efficiency. Combining quantum computing principles with deep learning techniques provides a game-changing answer to the growing complexity of today's cybersecurity threats. © 2023 IEEE.

关键词： Support vector machines

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Modern Systems and Algorithms for Enhanced Personal Well-being Data Management

Modern Systems and Algorithms for Enhanced Personal Well-bei...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Singh, Shweta Singh, Rajiv Ranjan Rengarajan, A. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

In this era of pervasive connection and data collecting, it is more important than ever that people have access to and control over their own health data. This presentation offers a comprehensive overview of the most recent techniques and algorithms for the expedited processing of health-related data. Individuals may now gather and track an unprecedented amount of data about their health and fitness thanks to wearable devices, smartphone applications, and Internet of Things (IoT) sensors. The potential benefits of better health outcomes, accelerated research, and patient empowerment may be enormous if this data could be managed correctly. This article digs into the innovative programs and algorithms that are changing the face of current healthcare data management. First, we examine the scalability, security, and interoperability of current data storage and retrieval techniques in detail. Some examples of information that might be collected, saved, and accessed with the use of such devices include physiological measurements, data on lifestyle choices, and medical records. This abstract also investigates potential future applications of innovative data processing and analysis techniques for gathering data on the health of the general public. Innovative methods for health data management, such as blockchain and federated learning, are discussed in depth in the abstract. By facilitating the safe and transparent exchange of data between academics, healthcare providers, and patients, technology developments like these foster collaboration and accelerate medical research. The study emphasizes the significance of users having control over and ownership of their data in health data management. Customizable dashboards, mobile applications, and user interfaces that are made as simple as possible allow people to actively engage with their own health data and make informed decisions. The vast potential of the health data landscape may be tapped with the help of state-of-the-art

关键词： information management

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