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Semar IoT Server Framework: Architecture and Technologies

Semar IoT Server Framework: Architecture and Technologies

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

作者： Menaka, C. Sahoo, Girija Shankar Sindgi, Anupama 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)9798350357769

Modern Internet of Things, or IoT, devices are developing quickly, which has raised the possibility of creating smart communities where collaboration and the combination of diverse IoT apps are crucial. But standards for devices, logic, and data transfers are often overlooked in the design and deployment of IoT apps. In this study, we propose the creation and development of the SEMAR IoT server platform for standard-based IoT application platform integration. SEMAR provides Big Data settings with pre-built features for data synchronizations, aggregations, and predictive classifications. Additionally, plug-in features are simple to build. Representations Event Transfer Application Development Interface solutions may take data from devices for various sensors immediately and via networks, it will be utilized in immediate form for screens, text files, and for connecting to other systems. For SEMAR examinations, we set up the platform and incorporated five connected device systems: the indoor translation system built around fingerprints, the water quality surveillance framework, the ecological observing system as a whole and the air superior track system. With operates for managing IoT devices, data exchange, choices, synchronizations, and barriers that could be easily coupled with exterior programmed or applications for IoT with no modification to the coding, the recommended SEMAR IoT applications server platforms give higher flexibility and compatibility in comparison to prior research on IoT server platforms. The results support the suggestion's effectiveness and efficacy. © 2023 IEEE.

关键词： Air quality monitoring Big Data settings Data processing Data synchronization IoT application systems MQTT protocol Real-time data collection SEMAR IoT server platform Smart cities Water quality monitoring

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts 3

Evaluative Analysis of Secure approaches in Simulated Real-W...

引用

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

作者： Kannagi, A. Chaudhary, Pavan Muthupandi, G. 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

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape. © 2023 IEEE.

关键词： Cybersecurity

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Powering up the IT Equipment and Cooling Units of Cloud Data Centers using Photovoltaic-fed Microgrid 2

Powering up the IT Equipment and Cooling Units of Cloud Data...

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Prusty, Sankata Bhanjan Behera, Liza Rani Roy, Pritha Singha Padhee, Subhransu Reva University School of Electronics and Communication Engineering Bangalore India Government College of Engineering Department of Computer Science and Engineering Keonjhar India Chitkara University Department of Computer Science & Engineering Punjab India Sambalpur University Institute of Information Technology Department of Electrical and Electronics Engineering Odisha Burla India

ISBN: (纸本)9798350320923

Cloud computing infrastructure is designed to support seamless accessability and availability of different services to consumers over the internet. Cloud data centers require a lot of power for powering up of the IT equipments and powering up of different air conditioning equipments. The high power consumption leads to higher energy bill as well as increased carbon foot print. To minimize the effect of carbon foot print, distributed energy resources are utilized. This paper provides a systematic analysis of the power model as well as air conditioning model of data centers. The architecture of solar microgrid and economic analysis of solar microgrid for data center has been highlighted in this paper. © 2023 IEEE.

关键词： Economic analysis

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A Comprehensive Survey of Machine Learning Strategies for Tumor Classification using Gene Expression Profiling

A Comprehensive Survey of Machine Learning Strategies for Tu...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Jeelani, Azra Jayabalan, Bhuvana Acharjya, Kalyan 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)9798350315127

Significant progress has been made in the field of tumor classification utilizing gene expression profiling in recent years. This research paper explores the potential of machine learning methods for tumor classification and suggests this approach as an alternative to current methods of diagnosis. The key performance measures used to evaluate these methods are the F1 score, accuracy, precision, recall, area under the receiver operating characteristic curve (AUC-ROC), and area under the receiver operating characteristic curve (AUC-PR). The proposed approach was put to the test alongside many others utilizing a dataset of gene expression profiles, including DecisionTree, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Random Forest, and Principal Component Analysis. It was established that the proposed technique outperformed the alternatives. The study shows that the newly suggested method outperforms the state-of-the-art methods in every respect, demonstrating its superior accuracy and precision in tumor classification. Furthermore, the current method is notably more computationally efficient, requiring only two hours of computing time as opposed to the significantly longer amounts of time spent calculating by earlier approaches. In conclusion, our results emphasize the relevance of the proposed technique for future tumor classification studies. Its excellent processing efficiency and outstanding accuracy make it a viable tool for precise categorization of tumor subtypes based on gene expression data. This study also emphasizes the significance of deliberative decision-making when selecting and evaluating machine learning algorithms for use in biomedical applications. New methods for enhancing tumor classification are made possible by our research, and this in turn aids in the development of more accurate diagnoses, prognoses, and tailored treatment programs for patients. © 2023 IEEE.

关键词： Tumors

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Pioneering Approaches for Accurate Vocal Anomaly Detection using State-of-the-Art Machine Learning Models

Pioneering Approaches for Accurate Vocal Anomaly Detection u...

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

作者： Kannagi, A. Acharjya, Kalyan Prabhu, T. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

Accurate Vocal Anomaly Detection (AVAD) has emerged as a critical area of research with far-reaching implications for healthcare, security, and human-computer interaction. This paper presents pioneering approaches for AVAD using state-of-the-art machine learning models. Leveraging the power of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVMs), our proposed method demonstrates remarkable efficacy in identifying vocal anomalies indicative of various health conditions and security threats. In the data preprocessing phase, we extract Mel-Frequency Cepstral Coefficients (MFCCs) as vocal features, providing a robust foundation for subsequent analysis. The CNN, adept at capturing local patterns and hierarchies in feature maps, showcases impressive performance in classifying vocal data. LSTMs, renowned for capturing temporal dependencies in sequential data, enhance the model's ability to detect anomalies within vocal patterns. SVMs, with their capacity to construct optimal hyperplanes in feature space, serve as an additional valuable tool for accurate anomaly detection. Our comprehensive evaluation incorporates a range of performance metrics, including accuracy, precision, recall, F1 score, ROC-AUC, sensitivity, and specificity. The results demonstrate that our proposed method excels in accurately detecting vocal anomalies across diverse use cases, opening doors to early diagnosis, healthcare monitoring, and enhanced security measures through vocal analysis. © 2023 IEEE.

关键词： Learning systems

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An Operative Investigation of ML Methods for Image Retrieval with MPKDC-Based Brain Image Segmentation 3

An Operative Investigation of ML Methods for Image Retrieval...

引用

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

作者： Manaswini, R. Ganesh, D. Kumar, Ritesh 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)9798350343274

Processing images is crucial in making judgements based on a wide range of criteria. Image processing techniques include enhancing, restoring, and compressing photos all help save space in the long run. Segmentation is a crucial stage that plays a significant role in every aspect of the image processing workflow. When assessing medical images, image segmentation-based improvement techniques are frequently used. This work proposes a unique MPKDC method for determining which hypoechoic areas in a brain imaging are most likely to contain a tumor. The proposed algorithm can help the radiologist determine what areas need to be scanned using MRI for diagnostic purposes. Multimodal Brain Tumor Image Segmentation datasets were used to evaluate the proposed method. This dataset includes 220 high-quality brain tumor images and 54 low-quality tumor images. Promising efficiency of the suggested segmentation methodology was demonstrated via cross-validation of the succeeding methods. © 2023 IEEE.

关键词： Image segmentation

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Brain Image Segmentation via GLCM Features and CNN Classification for Improved Image Retrieval Using Machine Learning 3

Brain Image Segmentation via GLCM Features and CNN Classific...

引用

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

作者： Sekar, R. Harikrishnan, P.R. Acharjya, Kalyan 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)9798350343274

Improvement methods based on picture segmentation are commonly employed in medical image analysis. Pixels are commonly put together in groupings of varied sizes to generate an image. The available pixel intensities in an image allow for the separation of distinct image regions. Edge-based and boundary-based segmentation methods are the two primary classifications of this type of analysis. The boundary-based segmentation technique is the more widespread of the two. The image of the tumor needs to be segmented into numerous fragments, each of which can then be analyzed separately. It can annotate data and locate locations of interest in medical image processing. The issues with the existing paper are addressed in the suggested study. Feature extraction using GLCM has been used for classification of tumor images. Analysis of data in segments is routine. The CNN classifier's segments are labelled by an analytical procedure. The proposed method boosts information quality and reduces error-prone training in MRI brain tumor pictures by increasing the learning coefficient of the back propagation layer in the neural network. As a result, the current CNN Shallow Net architecture achieves a higher classification rate of 99.61% thanks to its increased specificity (98.56%) and sensitivity (98.12%). The deep web achieves a segmentation accuracy of 99.58 percent, with an additional diagnostic accuracy of 98.5 percent in identifying cancerous spots. © 2023 IEEE.

关键词： Image segmentation

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Unstructured Healthcare Statistics Exploration Consuming Modest Ensemble Machine Learning Methods

Unstructured Healthcare Statistics Exploration Consuming Mod...

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

作者： Gunavathy, K. Agarwal, Ankita Sharma, Nipun Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

Various approaches to classifying emotions can be found inside an ML study. Of course, there are ML algorithms that do exceptionally well on particular text datasets but underwhelm on other, larger datasets. To get over this issue, several classifiers should be combined to boost classification performance across all datasets. In order to select the most effective classifiers for use in an ensemble, this study presents a competitive ensemble classification technique. The effectiveness of sentiment classification should improve as a result of this. Unstructured healthcare data is discussed together with the proposed competitive ensemble classification algorithm. This method employs Twitter data extraction to do multiclass sentiment categorization. They have been categorised into one of five mood groups. In this study, we propose a model that uses a competition between several types of classifiers to determine which ones will work best for ensemble learning. To enhance classification performance, weights are assigned to five separate classifiers, and both the suggested model and a benchmark are assessed. The proposed approach, based on empirical evidence, outperforms baseline categorization methods. To evaluate the effectiveness of the work proposed, experimental results are compared to standard datasets. The outcomes show that the proposed model is superior, with an increase in classification accuracy. © 2023 IEEE.

关键词： Benchmarking

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A Comprehensive Examination of Cloud-Enabled Frameworks for Power Management Systems: Current State, Challenges, and Forward-Looking Guidance in Big Data Analytics

A Comprehensive Examination of Cloud-Enabled Frameworks for ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Singh, Vaishali Dass, Sandhya Prakash, Febin Maharishi School of Engineering & Technology Maharishi University of Information 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)9798350315127

A Comprehensive Examination of Cloud-Enabled Frameworks for Power Management Systems: Current State, Challenges, and Forward-Looking Guidance in Big Data Analytics' examines cloud-enabled power management system frameworks in depth. This article provides a thorough evaluation of cloud-enabled power management system frameworks. This article examines the present state of power management systems from a range of perspectives. It emphasizes the importance of shifting away from traditional, locally focused methodologies and toward big data analytics-driven, cloud-based solutions. In this study, we look at the wide-ranging implications that cloud-enabled frameworks have had on a variety of sectors, including smart grids and data centers. It is hard to exaggerate the significance of these frameworks in handling the exponential expansion of data while also improving efficiency, scalability, and adaptability. This research acknowledges the relevance of data security and privacy, the necessity for interoperability and standardization in this rapidly expanding environment, and the difficulty in managing the huge volumes of data created by contemporary energy systems. This is achieved while stressing the numerous benefits of cloud-hosted data centers and intelligent grids. The paper also underlines how advantageous cloud-based data centers and smart grids are to their respective users. The essay proposes potential solutions to these difficulties in the form of future recommendations. © 2023 IEEE.

关键词： Data Analytics

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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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