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

作者： Sindgi, Anupama Adil, Mir Chaudhary, Pavan 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

Our everyday lives are being dramatically changed by the Internet of Things (IoT), but it is also introducing significant security vulnerabilities that might be misused by bad actors. This paper provides a thorough summary of past studies on security of Internet of Things devices by delving into a wide range of methods used for IoT device evaluation and the related issues. We also review 1oT-based security trials that have been described in scholarly works. In this study, we provide a free platform made to detect communicative and network issues in IoT systems. Its versatility and adaptability enable the simple incorporation of extra security inspection features and tests. Our novel method simplifies security assessments by enabling automated testing without direct human involvement. The lab claims the capacity to detect and avert possible inbound assaults in addition to being skilled at locating and making public security flaws in closely examined equipment. It also has the ability to keep an eye on how the machines in the testbed interact with one another and to step in and stop operations if it notices any suspicious activity. In this work, we examine two separate Internet of Things (or IoT) devices-a smart light and a linked video camera-for privacy vulnerabilities. This illustrates the efficacy of our suggested Internet of Things, also known as IoT, safety testbed. © 2023 IEEE.

关键词： Automated security evaluations Automated testing and IoT safety hazards Extensive analysis Internet of Things IoT deployments Security assessment Security flaws Security vulnerabilities Vulnerability detection

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loT Security Enhancement: A Cybersecurity Framework

loT Security Enhancement: A Cybersecurity Framework

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

作者： Tony Aby Varkey, M. Adil, Mir Singh, Swati 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

The use of Internet of Things (loT) in the development of electronic grids, smart neighborhoods, and other imaginative concepts has facilitated managers' use of data to make decisions and increased automation in each area. However, the poor standard cybersecurity in connected devices et the a state of hyper created by loT networks heighten security concerns that might endanger the everyday activities of medical centres, towns, and other organisations. The likelihood of attacks may be reduced by enhancing loT devices' security features before integrating them into other platforms. The goal of this research was to simulate a set of seven stages that would be used to execute hardening operations and reduce the attack surface. used the Preferred Reporting Materials from Systematic Studies and meta-analysis methods to carry out a comprehensive literature review. By using an agenda that takes into account the security elements in the three levels of the loT design, we were capable of to establish a recommended approach for evaluating the level of safety of an loT product. To assess the attack surface, a risk matrix tailored to loT is created. Finally, a method of strengthening and vulnerability evaluation is suggested to lower the attack surface and raise an Internet of Things (loT) solution's safety rating. © 2023 IEEE.

关键词： Attack surface Cyber threats Cybersecurity framework Internet of Things IoT hardening IoT security enhancement Risk analysis Security posture and ISO standards Vulnerability assessment

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Application of Machine Learning and Cloud Computing for Observing Health Status of Patients Remotely in Healthcare System

Application of Machine Learning and Cloud Computing for Obse...

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Manivasagam Kumar, Anshu Murshleen, Mohd. Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Strategies that may gain insight from both labeled and unlabeled data as well as from their environment are being developed through the use of machine learning. Machine learning is applied in many domains, but it is most commonly used in the healthcare sector, where it offers substantial benefits through suitable decision- and prediction-making techniques. A online system which can continuously monitor the individual's condition and generate an alert when needed is introduced by the proposed work. This suggested methodology uses cloud computing and various machine learning algorithms to store data continuously. To identify risks and provide the right treatment and diagnosis, physicians analyze medical data using machine learning techniques and tools. The range of remote healthcare services includes monitoring elderly patients, premature children, patients with chronic illnesses, and victims of accidents. The current study investigates how machine learning technologies can be used to monitor patients who are located remotely and provide real-time alerts about their condition via a remote system. © 2023 IEEE.

关键词： cloud computing machine learning remote healthcare Remote patient monitoring

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The Rise of Advanced Computational Techniques for Holistic Analysis of Human Gaze Behavior and Its Significance

The Rise of Advanced Computational Techniques for Holistic A...

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

作者： Srilakshmi, K.H. Saha, Ananya Sahoo, Girija Shankar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

The Rise of Advanced Computational Techniques for Holistic Analysis of Human Gaze Behavior and Its Significance Gaze behavior analysis has emerged as a crucial area of study with broad implications for psychology, neuroscience, human-computer interaction, and more. In this research, we introduce an innovative methodology that leverages advanced computational techniques to provide a holistic analysis of human gaze behavior. Our proposed method, GazeNet, combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Gaussian Mixture Models (GMMs) to capture both temporal and spatial aspects of gaze data. GazeNet excels in predictive modeling, enabling real-time applications in diverse fields. In this study, we compare the performance of GazeNet with ten original methods, including Visual Gaze Analysis, Eye-Tracking Analytics, and Gaze Behavior Pattern Recognition. The comparison includes accuracy, precision, recall, and F1 score, revealing that GazeNet consistently outperforms its counterparts. Furthermore, GazeNet provides comprehensive insights into cognitive processes, surpassing traditional methods. Our research signifies the transformative potential of GazeNet in psychology, neuroscience, clinical diagnostics, and real-world applications like user experience design and driver safety. The integration of advanced computational techniques brings a new level of precision and efficiency to gaze analysis, enabling a deeper understanding of human attention allocation. © 2023 IEEE.

关键词： Eye tracking

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of electronics and information Engineers.

关键词： Cavity resonators

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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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Maximizing Insights from MRI Brain Images Segmentation through HSV Histogram and Gabor Wavelet Transform, and Machine Learning-Assisted Image Retrieval 3

Maximizing Insights from MRI Brain Images Segmentation throu...

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

作者： Yadav, Dhyan Chandra Sharma, Nipun Kudari, Jayashree M 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)9798350343274

Medical imaging has better managed disease and reduced patient suffering by detecting brain tumours. Medical professionals employ magnetic resonance imaging (MRI). Due to its non-invasiveness and high resolution and contrast, MRI can see tissues. MRI may take several images of the same tissue region with improved contrast. Many photographs provide additional anatomical information about the same tissue. Data from many contrast mechanisms can help researchers understand brain illness. Because tumour tissue varies in location, size, shape, and texture, feature extraction is difficult. MR brain imaging components including white matter, grey matter, and cerebrospinal fluid have complicated structures that make feature extraction difficult. The literature on feature extraction for tumour segmentation does not compare which method is best. Here, we implement two commonly used libraries of established and successful texture feature extraction algorithms. By collecting frequency, localization, and orientation, Gabor wavelet feature extraction delivers multi-resolution surface information in both spatial and frequency domains. These feature extraction algorithms reflect the intensity relationship between image pixels or clusters of pixels. They may also predict image quality using first- and second-order statistics. Our research examines the efficacy and complexity of these two feature extraction algorithms for tumour diagnosis and segmentation. The researchers employed a locust-based genetic algorithm to analyse MR images perimage, reducing the likelihood that the results were just machine-Iearning- based. © 2023 IEEE.

关键词： Tumors

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Ensemble Learning for Multi-Class Classification in Cervical Cancer Risk Factor Analysis: An Empirical Computational Approach 3

Ensemble Learning for Multi-Class Classification in Cervical...

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

作者： Chaudhuri, Sumantra Suneetha, K. Yadav, Rakesh Kumar 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

One cancer that affects women of all ages frequently and is also particularly fatal is cervical cancer. It is fatal because it cannot be anticipated in the early stages when the patient could be treated properly. When cervical cancer cells begin to migrate to nearby organs including the liver, lymph nodes, and lungs, they block the tube that takes urine from the bladder and cause symptoms. Leg discomfort, exhaustion, weight loss, vaginal bleeding, bone fractures, and pelvic pain are among the symptoms in this latter stage. Human papillomavirus (HPV), smoking, numerous pregnancies, using hormonal contraception, and other risk factors include cervical cancer. Screening is a crucial step in the detection of cervical cancer. This study suggests a technique for categorizing cervical cancer risk variables into four separate classes using a multi-class approach. The ensemble learning approach aims to increase the suggested model's accuracy. This technique aids in enhancing the study of risk factors used to determine the likelihood of developing cervical cancer. A multi-layer feed-forward network is used to examine the suggested Artificial Bee colony (ABC) along with a deep learning method known as the LST Model for various screening methods. The gradient boosting method with Multi-Layer Perceptron (MLP) is found to produce a higher % for all performance measures using the suggested model. © 2023 IEEE.

关键词： Learning systems

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Leveraging Deep Learning Techniques for Profiling and Categorizing Lung and Pancreatic Tumors

Leveraging Deep Learning Techniques for Profiling and Catego...

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

作者： Jeelani, Azra Vaishnawi, Chittamuru Yadav, Rakesh Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

The study provides a unique strategy for "Leveraging Deep Learning Techniques for Profiling and Categorizing Lung and Pancreatic Tumors."The suggested technique incorporates Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for temporal modeling, and Transfer Learning to fine-tune pre-trained models. It aims to improve tumor classification precision by tackling the particular challenges given by pancreatic and lung cancers. The outcomes of our investigation show that the proposed strategy outperforms existing methods in numerous key areas. F1 was 0.85, specificity was 0.87, recall was 0.82, accuracy was 0.85, precision was 0.88, and area under the curve was 0.92, according to the results. These values routinely exceed six original conventional approaches, including CNNs, RNNs, Transfer Learning, Autoencoders, Support Vector Machines (SVMs), and Reinforcement Learning. The confusion matrix measurements further underscore the superiority of the suggested strategy with more true positives and true negatives, and fewer false positives and false negatives. In conclusion, the suggested strategy improves the accuracy and objectivity of lung and pancreatic tumor profiling by harnessing the power of deep learning and a multi-faceted approach. It offers a more tailored, efficient, and data-driven approach to the arduous issue of tumor classification. The data clearly illustrate its superiority over traditional approaches, making it a potential innovation in the field of cancer. © 2023 IEEE.

关键词： Convolutional neural networks

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IoT Driven Air Quality Surveillance and Forecasting

IoT Driven Air Quality Surveillance and Forecasting

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Singh, Daljeet Pal Aruna, M. Somanna, M. N. Varun 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)9798350344387

In order to lessen dependency on expensive sensors, this study introduces a low-cost model that makes use of computing power at the edge and the Internet of Things in an effort to develop affordable air quality monitoring and predictive platforms. The Raspberry Pi, also known as a device that provides edges with computing power, applies the Kalman Filter (KF) method to improve low-cost measurements by 27% at the boundary of the device. By combining data with related mistakes, the methodology uses the KF algorithm to predict the level of six pollutants in the air, including SO2, NO2, and PM2.5, with lightning speed. The KF procedure's higher predictive ability is shown by comparisons with three traditional prediction techniques: Autonomous Integrated Moving Standard, Increasingly Weighted Move Average, and Simple Moving Average, among others. The root-mean-square error measurement showed a significant decrease of 68.3% on averaged. These results highlight the system's potential to promote intelligent farming and are consistent with its potential for implementation in several domains where precise, immediate time quality of air monitoring is essential. © 2023 IEEE.

关键词： Air quality surveillance Edge computing Forecasting Internet of Things (IoT) IoT-driven Kalman Filter Low-cost sensors Real-time prediction and Machine learning algorithms Smart agriculture

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