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UKsim/AMss 1st International Conference on Intelligent systems, Modelling and simulation, IsMs 2010

作者： Ramesh babu, H.s. Gowrishankar satyanarayana, P.s. Department of Information Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Department of Electronics and Communication Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9780769539737

The Next generation wireless network (NGWN) will be heterogeneous in nature where different radio access technologies (RATs) operate together .The mobile terminals operating in this heterogeneous environment will have different quality of service requirements to be handled by the system. The radio resource management is one of the key challenge in *** admission control is one of the radio resource management technique plays instrumental role in ensure the desired Quality of service (Qos) to the users working on different applications which have diversified nature of Qos requirements from the wireless networks . The call blocking probability is one such Qos parameter for the wireless network. For better Qos it is desirable to reduce the call blocking probability. In this customary scenario it is highly desirable to obtain analytic Performance model. In this paper we propose a higher order Markov chain based performance model for call admission control in a heterogeneous wireless network environment. In the proposed algorithm we have considered three classes of traffic having different Qos requirements and we have considered the heterogeneous network environment which includes that can effectively handle applications like voice calls, Web browsing and file transfer applications which are with varied Qos parameters. The paper presents the call blocking probabilities for all the three types of traffic both for fixed and varied traffic scenario. © 2010 IEEE.

关键词： Quality of service

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Deep-Q Classifier for Predicting balanced and Imbalanced Features in Cartpole and Lunarlander Dataset

Deep-Q Classifier for Predicting Balanced and Imbalanced Fea...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDsAAI 2023

作者： veeramanikandan, K. Ponnusamy, R. V.R.S. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Villupuram India Chennai Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

The purpose of this paper is to investigate the impact of significant Q-Assignment (sQN) on the convenience of multiclass portrayal estimations. To create a framework that blends DQN with existing supervised multiclass classification algorithms, we will utilize a Kaggle benchmark dataset. The findings of this review will provide insight on how to utilize profound support learning methods to enhance multiclass order precision. They have been used in different fields, including picture affirmation, normal language processing, and bioinformatics. The specialist demonstrates one example at each time point, and the climate evaluates the order activity and gives the specialist a prize. The specialist is more attentive to the minority class due to the higher prize for minority class tests. The agent ultimately discovers the optimal classification policy in the data with imbalances, guided by a specific reward function and a beneficial learning environment. © 2023 IEEE.

关键词： Classification (of information)

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Facial emotion recognition using convolutional neural networks

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Materials Today: Proceedings 2023年 80卷 3560-3564页

作者： sarvakar, Ketan senkamalavalli, R. Raghavendra, s. santosh Kumar, J. Manjunath, R. Jaiswal, sushma Information Technology Department U.V. Patel College of Engineering Ganpat University Gujarat Mehsana384012 India CSE Department East Point College of Engineering Bangalore India Department of Computer Science and Engineering School of Engineering and Technology Christ Deemed to be University Bengaluru560074 India Department of Computer Science and Engineering K S School of Engineering and Management Bengaluru56019 India Department of Computer Science and Engineering R R Institute of Technology Bengaluru560090 India Bilaspur495009 India

Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (Ev) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, Ev generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). © 2021

关键词： Emotion Recognition

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An efficient secure algorithms to mitigate Dos, replay and jamming attacks in wireless sensor network

An efficient secure algorithms to mitigate DoS, replay and j...

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2018 World Congress on engineering and computer science, WCECs 2018

作者： Marigowda, C.K. Thriveni, J. Gowrishankar, s. venugopal, K.R. Department of Information Science and Engineering Acharya Institute of Technology Visvesvaraya Technological University Bengaluru India Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bengaluru India Department of Computer Science and Engineering B M S College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bengaluru India

ISBN: (纸本)9789881404817

Wireless sensor Networks are by nature resource constrained, due to this characteristic, they are more vulnerable to different kinds of attacks. Protecting unauthorized users from accessing information stored in WsN is an important issue to be addressed. The proposed synchronized Incremental Counter (sIC) mechanism focuses on security in WsN against Jamming and Replay attacks. Modified Constrained Function based Authentication (MCFA) algorithm is an AEs with OCb mode, a symmetric key cryptography is used against Denial of service (Dos) attack. The proposed collective methods effectively provide security without degrading the network performance with less packet loss and communication time, thus increasing the network lifetime. © 2018 Newswood Limited.

关键词： Wireless sensor networks

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LoRaWAN and biosensor-Assisted smart City Framework to Mitigate the Effects of Lung Diseases

LoRaWAN and Biosensor-Assisted Smart City Framework to Mitig...

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2024 IEEE International Conference on Modeling, simulation and Intelligent Computing, MosICom 2024

作者： Abubeker, K.M. Roberts, Michaelraj Kingston Prajitha, C. Rinesh, s. shafeena Karim, A. Jeevanandham, s. Department of Electronics and Communication Engineering Kerala India Sri Eshwar College of Engineering Centre for Advances in Signal Processing and Wireless Sensor Networks Department of Electronics and Communication Engineering Coimbatore India Karpagam Academy of Higher Education Centre for Interdisciplinary Research Department of Electronics and Communication Engineering Coimbatore India V.S.B College of Engineering Technical Campus Department of Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India

ISBN: (纸本)9798331533311

Urbanization and industrialization in cities present substantial obstacles to air quality, resulting in detrimental effects on public health, including respiratory health. This research introduces an Internet of Things (IoT) framework for a smart city environment using LoRaWAN and sensor networks. The framework utilizes the EsP32 LoRaWAN module to provide real-time monitoring of essential air quality metrics, such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, and humidity. Integrating biosensors enhances accuracy by identifying distinct biological indicators linked to respiratory well-being. The proposed work aims to use the framework in an urban setting within a global context, demonstrating its capacity to reduce the effects of lung disorders. The EsP32 LoRaWAN module enables effective communication within the sensor network up to 20 kilometers, guaranteeing a robust and adaptable solution for smart city applications. The developed framework provides a scalable and novel method for developing healthier and more sustainable cities, decreasing respiratory disorders as urban settings develop. © 2024 IEEE.

关键词： Lung cancer

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Activity Monitoring for ICU Patients Using Deep Learning and Image Processing

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sN computer science 2020年第3期1卷 1-8页

作者： Magi, Nikitaa Prasad, b.G. Department of Computer Science and Engineering B. M. S. College of Engineering Bull Temple Road Basavanagudi Bangalore 560019 India

A patient in coma condition needs intense monitoring and care, but nowadays shortage of intensivist and critical care nurses is the major problem faced by the hospitals. Traditional method of monitoring ICU patients requires dedicated proactive observer to look at the patient all day long. One of the issues with traditional methods is that the observer has to be available all 24 h of the day and one observer will be able to monitor only one patient at a time. This paper aims to develop a system which overcomes these issues faced by traditional methods. The proposed system uses image processing and deep learning approach, with which the system can monitor the patients in critical care unit and can report to the doctor or nurse. The proposed system is capable of detecting target object (patient) in real time, further detect patient’s activity if any and notify the doctor or nurse. © 2020, springer Nature singapore Pte Ltd.

关键词： Artificial intelligence Deep learning Human activity analysis Image processing Neural network Object detection Patient monitoring

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Hybrid multichannel Wireless Mesh Network (HMWMN) performance analysis using IEEE 802.11

Hybrid multichannel Wireless Mesh Network (HMWMN) performanc...

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4th International Conference on the Next Generation Information Technology summit, Confluence 2013

作者： Naragund, Jayalakshmi G. banakar, R.M. Department of Computer Science and Engineering B V B College of Engg. and Tech. Hubli India Sriharikota India Department of Electronics and Communication Engineering B V B College of Engg. and Tech. Hubli India

ISBN: (纸本)9781849198462

Wireless Mesh Network (WMN) is one of the prominent technologies in the wireless network field. The main goal of WMN is to serve the large community of users with available bandwidth. Hyacinth multichannel architecture is preferable solution for WMN to lever the substantial data and to employ the bandwidth efficiently. The performance of WMN is also depending on the routing protocol to forward the data in the network. The AODv is the most common routing protocol of wireless network technologies and it uses hop count as routing metric. This traditional routing metric is incapable to perform well in multichannel environment. Weighted Cumulative Expected Transmission Time (WCETT) routing metric is well suited for multichannel WMN. In this paper we analyze the performance of Hybrid Multichannel WMN. Two channels are assigned to the nodes and IEEE 802.11 channel assignment strategy is used by HMWMN. Throughput and drop of packets parameters are compared with single channel WMN and HMWMN by varying packet size. We conduct the Chi square test to show the relationship between packet size and throughput. Our results show that HMWMN improves the throughput by 46% and minimizes the drop of packets by 62% than the single channel. HMWMN uses WCETT routing metrics and it is compared against hop count by varying the size of network. Outcomes illustrate that HMWMN has better throughput and End-to-end delay even for large WMN.

关键词： Routing protocols

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Time-series Cryptocurrency Forecasting Using Ensemble Deep Learning

Time-Series Cryptocurrency Forecasting Using Ensemble Deep L...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Rao, K Rama Prasad, M Lakshmi Kumar, G Ravi Natchadalingam, R. Hussain, Mohammad Manzoor Reddy, Pundru Chandra shaker Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Hyderabad India SRM Madurai College for Engineering and Technology Computer Science and Engineering Tamilnadu Madurai India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Cryptocurrency is now widely accepted as a payment and exchange method, permeating nearly every aspect of the financial sector. similar to the non-stationary and very erratic price movements of traditional stocks, cryptocurrency price swings are highly unpredictable. The rising popularity of cryptocurrencies has prompted an increase in the number of studies conducted to predict their future prices. The importance of cryptocurrency forecasting has grown significantly with the advent of deep learning. portfolio optimisation and decision making cannot be achieved without the creation of a smart forecasting model. The primary contribution of this study is the utilize of traditional deep learning (DL) models in combination with the three most popular ensemble learning algorithms (ensemble-averaging, bagging, and stacking) to predict the hourly values of major cryptocurrencies. Traditional DL strategies consisting of combinations of long short-term memory (LsTM), bi-directional (biLsTM), and convolutional layers were utilized to assess the suggested ensemble methods. The ensemble techniques were tested on their capacity to forecast the price of a cryptocurrency an hour basis (regression) and to determine whether the price will rise or fall relative to the present (classification). Our in-depth experimental research shows that combining ensemble learning with deep learning can produce robust, stable, and trustworthy forecasting strategies. © 2023 IEEE.

关键词： Long short-term memory

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Prominent boundaries and foreground detection based technique for human face extraction from color images containing complex background

Prominent boundaries and foreground detection based techniqu...

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2011 3rd National Conference on computer vision, Pattern Recognition, Image Processing and Graphics, NCvPRIPG 2011

作者： Thakore, Darshak G. Trivedi, A.I. Computer Engineering B v M Engineering College Vallabh Vidyanagar India Faculty of Technology and Engineering M. S. University Vadodara India

ISBN: (纸本)9780769545998

The paper proposes a novel method for human face extraction from color images, characterized by uncontrolled illumination conditions and complex backgrounds. The method incorporates stationary Haar wavelet transform & proximity influence for prominent boundaries detection and watershed transform, proximity influence & morphological operations to separate foreground / background along with region and color attributes for human face extraction. The method exploits redundancy by coalescing local color cues of all color channels to emphasize reliable processing to precisely detect the human face by avoiding under-segmentation and reducing over-segmentation & artifacts. The method has been tested on face-image collection of standard database and on images captured by an amateur photographer for various complex backgrounds having diversified textures, varied illumination conditions and multiple background objects. The presented results show the effectiveness of the method for human face extraction, proving it suitable as an input to applications like digital album catalogue, content based image retrieval, face recognition and facial expression recognition. © 2011 IEEE.

关键词： Object detection

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A Novel Approach to Operate Appliances: Remotely Enabled Communication system 14

A Novel Approach to Operate Appliances: Remotely Enabled Com...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Mishra, sarang Krishnakumar Dahiwale, Karan Krishna shende, vinay sanjay Hira, swati Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering S.B Jain Institute of Technology Management and Research Nagpur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research Nagpur India

In recent years, a lot of research work has been proposed on routing scheme in WsN. Remote Accessing is one of the most promising techniques due to its simplicity, scalability and efficiency. Under many research and development cell, it has been noted that establishing the wireless sensor nodes and let them work as a cluster, but the major problem with the wireless technology has limitation of range. The solution of this issue is to use Radio frequency as a way to communicate in a cluster. As REMs mainly focuses on the Agricultural sector, where the lands are present remotely with issues of Internet connectivity which is the main requirement for the present IOT devices. That is why we aim to develop a system which entirely runs on its own private network without any need of Internet connectivity. The system is easy to use and has a long range. The system also doesn’t require a smart phone as it is interface free, it can be controlled with a simple keypad phone. The cost effectiveness and the mentioned features makes REMs a system, which can independently survive in remote agricultural lands. Thus, the creation of this system will help farmers for controlling the devices which run on electricity (e.g Irrigation Motors) as the system is cost effective, has long range and a internet free technology. © Grenze scientific society, 2023.

关键词： Cost effectiveness

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