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An Adaptive Multilevel Location Based Key Management System For Dynamic Wireless Sensor Networks

International Journal of Applied science and engineering 2021年第1期18卷 1-18页

作者： Arumugam, Venkatesh Seshasayanam, Asha School of Computing Science and Engineering VIT University Chennai Campus India

A Key management system plays an important role in the process of wireless communication between the nodes of a Wireless Sensor Network (WSN). Unlike the wired networks, WSNs are more vulnerable to attacks from the malicious nodes. To overcome the shortcomings of the existing key management systems, this paper proposes an adaptive multilevel location-based key management system (AML-KBS), in which the keys are generated dynamically and shared among the nodes of the wireless networks. Since the proposed approach follows a location-based system for key management, the attackers can be differentiated based upon their location. Also, the proposed system has proven its withstanding against node capture attacks. Comparing with the existing approaches, the memory requirement of the proposed system has shown better improvement. Moreover, the proposed methodology provides better security mechanism than the existing key management systems. © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted distribution provided the original author and source are cited.

关键词： Key management Local node attacks Node capture attack Wireless sensor networks

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Hybrid Deep Learning and Ensemble Methods for Alzheimer’s Detection from EEG Signals 3

Hybrid Deep Learning and Ensemble Methods for Alzheimer’s D...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Anjali, Mokara Sundar, G. Naveen Meghana, Yetukuri gana bala Sreeja, Beereddy Narmadha Thangavel, Senthil Kumar Karunya Institute of technology and sciences Coimbatore India Division of AIML Karunya Institute of technology and sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331532420

Alzheimer's disease (AD) is a progressive neurological disorder characterised by aberrant behaviour, memory loss, and cognitive impairment. Electroencephalography (EEG) is an efficient method that provides valuable information on brain activity and can be used to predict AD. The application of EEG data for AD classification is examined in this abstract, with particular attention paid to the study of frequency bands, and Machine Learning (ML) *** algorithms classifies AD with excellent levels of accuracy, indicating possibility of EEG-based methods for early disease identification. In order to classify AD in its early stages, this research proposes a unique deep learning model based on convolutional neural networks (CNNs) and signal processing techniques. The proposed model aims to classify patients into two categories: AD positive and negative. At the early stage the dataset was preprocessed using techniques such as label encoding and noise removal. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) are the techniques applied in signal processing. The classification is done using Deep Learning (DL) and Machine Learning (ML) methods are used to develop the best model. ML algorithms namely Stacking classifier, Gradient Boosting, Stacknet, AdaBoost, and DL algorithms like Convolutional Neural Networks (CNN) and Deep Neural Network (DNN) architectures have shown promise in AD classification. Random Forest and AdaBoost excel in ensemble learning, while CNN and DNN models capture intricate patterns in EEG data, further enhancing the accuracy of diagnosis. In conclusion, EEG data offer non-invasive and economical method for classification of AD and contain useful information. © 2024 IEEE.

关键词： Convolutional neural networks

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Controlling of Cursor movement with Eyeball using Hidden Markov Model

Controlling of Cursor movement with Eyeball using Hidden Mar...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： P Havirbhavi G Tanusha K Ashwini Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai

In this study, a novel method for moving cursor positions using eye-ball movements with the aid of HMM. Conventional cursor control techniques frequently depend on physical input devices like touchpads or mice, which restricts accessibility for those with motor disabilities. By utilizing the accuracy of eye-tracking technology, our solution creates a smooth interface between digital engagement and human visual attention. We examine the complex patterns of eye movements as users move through their visual environment using HMM. An innovative development in human-computer interaction is the incorporation of an intuitive and adaptable control system that converts complex patterns of eye gaze data into cursor motions. This novel method makes use of state-of-the-art machine learning techniques to identify the relationships between cursor behavior and eye movements. This allows for a degree of accuracy, awareness of context, and instantaneous control over the cursor that is not possible with conventional input techniques. By automatically matching gaze patterns to cursor movements, this intelligent system not only improves user experience but also continually adjusts to the unique behaviors of each user, resulting in a smooth and customized connection with technology. A new frontier in the merging of machine intelligence and human cognition is reflected in the dynamic and responsive interface that emerges as a result. Because of its versatility, our proposed method can improve user experience and overall efficiency by learning and adjusting to the unique behaviors of each user.

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A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Kumar, Senthil Atyam, Nageswara Rao Khadar Nawas, K. Awasthi, Shashank Kotteeswaran, M. Thirunavukkarasu, K.S. Koneru Lakshmanan Education Foundation Kl School of Architecture Andhra Pradesh Guntur India Cmr Institute of Technology Electrical and Electronics Department Karnataka Bengaluru India Vellore Institute of Technolog School of Computing Science and Engineering Tamil Nadu Chennai India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Greater Noida India School of Management and Commerce Tamil Nadu Pallavaram Chennai India School of Computing Science Tamil Nadu Pallavaram Chennai India

ISBN: (纸本)9798350323887

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards. © 2023 IEEE.

关键词： Foundations

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WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer 41

WISER: Weak Supervision and Supervised Representation Learni...

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41st International Conference on Machine Learning, ICML 2024

作者： Shubham, Kumar Jayagopal, Aishwarya Danish, Syed Mohammed Prathosh, A.P. Rajan, Vaibhav Department of Electrical Communication Engineering Indian Institute of Science Karnataka Bengaluru India Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Indian Institute of Technology Bihar Patna India

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (‘cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method WISER (Weak supervISion and supErvised Representation learning) over state-of-the-art alternatives on predicting personalized drug response. Our implementation is available at https://***/kyrs/WISER. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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EchoVision: RealTime Object Detection and Voice Assistance for the Visually Impaired

EchoVision: RealTime Object Detection and Voice Assistance f...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Penumarthi Hima Varshini Swati V D. Navya Aiswariya Milan K Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Visual impairment is one of the greatest challenges that individuals must overcome to access and navigate their environment. To address this crucial issue, the paper discusses an intelligent system that aims to improve the accessibility and independence of visually impaired people in their local communities. The proposed system uses an object detection model that interfaces with a text-to-speech system. It enables the user to express orally the names of objects which have been detected in their selected language and provides relevant information in the user’s native language. Through a comprehensive evaluation, the efficiency of this system has been carefully examined. The results show important improvements on the object detection score which will enable users to interact with their environments with greater confidence and more independence. This paper takes an important step in narrowing the access gap and bringing independence to visually impaired individuals.

关键词： Visualization Visual impairment Object detection User interfaces Real-time systems Text to speech Object recognition Intelligent systems Wearable devices Speech to text

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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An optimal container update method for edge-cloud collaboration

An optimal container update method for edge-cloud collaborat...

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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Comparison of Various Machine Learning Algorithms in Predicting Air Quality

Comparison of Various Machine Learning Algorithms in Predict...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Tanisha Madan Shraddha Sagar Deepali Virmani School of Computing Science and Engineering Galgotias University Greater Noida India School of Engineering and Technology VIPS-TC Delhi India

Air pollution is the major environmental issue that need to be controlled immediately as it is causing various life-threatening disease like cancer, pneumonia, asthma, and other breathing problems to human beings. It is also damaging plants and soil and our ecosystem is getting imbalanced. Various human activities like burning of fossil fuels, factories and industries are becoming the major cause of air pollution. But before controlling it is necessary to predict air quality in terms of Air Quality Index. Various statistical methods exist to predict air quality but due to manual calculation it lacks accuracy. Machine learning plays an important role in predicting air quality accurately as it learns from training data. In our approach dataset is collecting from Open weather API. Preprocessing of dataset is done, and normalization of dataset is done using Z-score method. Time series prediction is followed with a delay of one hour. Filtration of dataset and statistical history for a particular date is also calculated. Visualization of dataset is also done in terms of graph. Input parameters taken are PM10. PM25, SO2, NO2, CO and output parameters are Air quality index. Prediction is done using K-Nearest Neighbor, Random Forest, and Naïve Bayes. Random forest is giving better performance as compared to other algorithms in terms of accuracy and Naïve Bayes exhibit least accuracy. Precision and Recall is also calculated for each category.

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Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. Visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image classification

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