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4th International Conference on Mobile Technology, Applications and systems, Mobility 2007, Incorporating the 1st International symposium on computer Human Interaction in Mobile Technology, Is-CHI 2007

作者： Gowrishankar satyanarayana, P.s. Computer Science and Engineering B.M.S. College of Engineering Bangalore India Electronics and Communication B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9781595938190

bit error rate (bER) will enumerate the Channel state Information (CsI) in wireless network. Accurate and timely estimation of CsI will guarantee the Quality of service (Qos) by admission control, inter and intra network handovers. Here the bER of time varying Non line of sight (NLOs) channels are predicted by neural network system. The wireless channel is modeled as time variant nonlinear system. The neural network systems are the best suitable paradigm to predict and analyze the behaviors of time varying nonlinear system. In this framework bER is predicted by two different recurrent neural network architectures such as Recurrent Radial basis Function Network (RRbFN) and Echo state network (EsN). Copyright 2007 ACM.

关键词： Quality of service

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RsOM-based Clustering and Routing in WsNs 2nd

RSOM-Based Clustering and Routing in WSNs

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2nd International Conference on smart Innovations in Communications and Computational sciences ICsICCs-2018

作者： Asha, G.R. subrahmanyam, Gowrishankar Department of Computer Science and Engineering B M S College of Engineering Bangalore India Department of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9789811324130

Wireless sensor Networks (WsNs) play a vital role in data transmission based on the location of sensor Nodes (sNs). The WsN contains base station (bs) with several sNs and these nodes are randomly spread in the region. The bs is to give the commands and directions to the sN. Nowadays, an energy consumption and lifetime are the major issues in the WsN. Hence, an efficient clustering and routing mechanism are implemented based on a popular Neural Network (NN) concept: Recurrent self Organizing Map (RsOM) (RsOM-WsN). In this paper, the life time of sNs and energy consumption of the proposed method is compared with state-of-art techniques of clustering and routing in WsN: LEACH-WsN, PsO-PsO-WsN, FCM-PsO-GsO, and EbC-s. © 2019, springer Nature singapore Pte Ltd.

关键词： sensor nodes

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Analysis of Cyber security Attacks using Kali Linux

Analysis of Cyber Security Attacks using Kali Linux

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Gururaj H, L. soundarya b, C. Janhavi, v. Lakshmi, H. Prassan Kumar, M.J. Vidyavardhaka College of Engineering Computer Science and Engineering Mysuru India B.G.S Institute of Technology Computer Science and Engineering B.G Nagar India

ISBN: (纸本)9781665483162

In the prevailing situation, the sports like economic, industrial, cultural, social, and governmental activities are carried out in the online world. Today's international is particularly dependent on the wireless era and protective these statistics from cyber-assaults is a hard hassle. The reason for cyber-assaults is to damage thieve the credentials. In a few other cases, cyber-attacks ought to have a navy or political functions. The damages are PC viruses, facts break, DDs, and exceptional attack vectors. To this surrender, various companies use diverse answers to prevent harm because of cyberattacks. Cyber safety follows actual-time data at the modern-day-day IT data. so, far, numerous techniques have proposed with the resource of researchers around the area to prevent cyber-attacks or lessen the harm due to them. The cause of this has a look at is to survey and comprehensively evaluate the usual advances supplied around cyber safety and to analyse the traumatic situations, weaknesses, and strengths of the proposed techniques. Different sorts of attacks are taken into consideration in element. In addition, evaluation of various cyber-attacks had been finished through the platform called Kali Linux. It is predicted that the complete assessment has a have a study furnished for college students, teachers, IT, and cyber safety researchers might be beneficial. © 2022 IEEE.

关键词： Cybersecurity

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： shu-Chuan Chu Xiaomeng Yang Li Zhang václav snášel Jeng-shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. by amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Covertness analysis of subliminal channels in legitimate communication

Covertness analysis of subliminal channels in legitimate com...

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International Conference on Advanced Computing, Networking and security, ADCONs 2011

作者： Gururaja, H.s. seetha, M. Koundinya, Anjan K. Department of Information Science and Engineering B.M.S. College of Engineering Bangalore India Department of Computer Science and Engineering G. Narayanamma Institute of Tech. and Science Hyderabad India Department of Computer Science and Engineering R.V. College of Engineering Bangalore India

ISBN: (纸本)9783642292798

secure transaction over a network is one of the critical application which employs high-level cryptographic methods to secure information exchange. secure communication merely relies on the strength of a cryptographic method. However early research in 80's have explored possibilities of various loopholes in a cryptosystem, one of which is the attacks based on subliminal channel. This was first demonstrated by G. simmons for DsA Algorithm by planting covert or subliminal data in one of the variables in DsA. subliminal channel user embeds the covert data in traditional cryptosystem and this is termed as backdoor - an unintended design mathematically planted in any cryptographic algorithm. such attacks on secure network will compromise the most important attribute of network called "privacy". This paper explores all the theoretical boundaries involved with subliminal channels and proposes a new metric called "covertness index" which indicates the strength of the trapdoor or backdoor placed in the communication channels. This index assists in assessing or evolving a detection method to analyze the embodied trapdoor in a communication channel. This paper also proposes certain changes to elliptic curve model for generating randomness which is a vital step in creation of cryptographic keys in public-key cryptosystem. © 2012 springer-verlag.

关键词： Network security

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Ensemble Approaches for Offensive Language Detection in Kannada 3

Ensemble Approaches for Offensive Language Detection in Kann...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Monish, M.N. sooda, Kavitha B.M.S. College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble's performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs. © 2024 IEEE.

关键词： Genetic algorithms

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Noninvasive Wearable Device for Monitoring and Assisting Asthma Patients

Noninvasive Wearable Device for Monitoring and Assisting Ast...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Himani, b.M. Prakash, Dyuthi Abhitha Mahendra, Nandita Asha, G.R. B.M.S. College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Asthma is a chronic condition that affects the air passages in the lungs, causing symptoms such as cough, wheeze, shortness of breath, and chest tightness, which can be triggered by various factors including viral infections, dust, smoke, pollen, and soaps. It can affect patients' daily lives in many harsh, debilitating ways, severe cases can lead to emergency health care, hospitalization, and even death. Although asthma can't be cured, good management with inhaled medications can control the disease and enable people with asthma to lead a normal, active life. One of the ways in which asthma management becomes easier is the prediction of severity of asthma exacerbations in a patient. This model utilizes sensors and data collected from IoT devices and smartphones to predict asthma risk and severity. The model is trained on a dataset of asthma patients and takes into account various factors such as symptoms, triggers, and objective test results. The model is integrated with a non-invasive wearable device through bluetooth. The device itself adopts the latest IoT technologies to collect data about the patient's whereabouts, their triggers as well as the condition of their disease. As the wearable device collects information from the sensor, this data is stored in the web application, where it can be compared to the previously collected readings to predict the severity of the asthma patient. The web application provides an interface between the patient and the data collected for prediction. This system significantly benefits asthma patients by providing a way to manage their condition better. © 2023 IEEE.

关键词： Forecasting

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A conceptual analysis for the measurement of stress intensity by deep learning using EEG signals

A conceptual analysis for the measurement of stress intensit...

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Nikhil, A.s. banakar, Nitish N Jagadeesh, Pranav bhat M, Madhukesh selva Kumar, s. B.M.S College of Engineering Department of Computer Science and Engineering Bengaluru560019 India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

several Health-related issues are caused due to mental stress. In Initial stages the level of mental stress can be measured using tools which are developed by Physicians and scientists. Many neuroimaging tools have been suggested to measure the stress developed in the workplace. Electroencephalogram (EEG) signals are a key component as they contain vast knowledge related to conditions and states of mind. Existing methods for EEG signal analysis are discussed to measure level of stress in the paper. This review focuses the crucial variations in-between the research results and debates those fluctuations from the data analysis methods give rise to many contrary results. The fluctuations in results may be because of several factors like no proper protocol, the area of interest within the brain, stressor types, experiment period, EEG analysis, methodology used by which properties are extracted, and classifier type. Hence, the major factor relating to detection stress in mind, by choosing the major suitable properties. A convoluted come varied band of properties relating to EEG, with variation in time, working, and dynamic connections of brain, required inclusion of different methods to learn about their connections with mental stress. subsequently, the evaluation recommends merging cortical activations with the integration to the network calculates with approaches of deep learning to enhance the correctness range of mental stress calculation. © 2022 IEEE.

关键词： brain computer interface

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An Efficient Missing Data Imputation Model on Numerical Data 2

An Efficient Missing Data Imputation Model on Numerical Data

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2nd Global Conference for Advancement in Technology, GCAT 2021

作者： sowmya, v Kayarvizhy, N. B.M.S College of Engineering Department of Computer Science and Engineering Bangalore560019 India

ISBN: (纸本)9780738132150

With the rising spotlight on Data science and big data analytics in both private and public sectors, enormous amount of domain-specific information has been collected. Missing information is a typical issue and consequently has pulled in significant consideration. Incomplete data analysis is a practical problem, and an intriguing issue. This study presents a methodology and framework for imputing missing value and at different missing rate of variables. An effective missing data imputation model based using Long short-Term Memory (LsTM) on numerical dataset is proposed. For the hidden layers in a deep neural network, Rectified Linear Units (ReLU) is conventionally used as an activation function. However, there are situations where progressively more complicated functions are required to tackle vanishing gradient problem. The proposed LsTM with ReLU activation function is advantageous in terms of mapping features, weight distribution and loss function. We estimate LsTM-ReLU's hyper-parameter values and compared its average RMsE performance with another neural network LsTM model LsTM-softmax and machine learning models like Naïve bayes and KNN. Experiments were carried out on the various datasets. Results obtained using LsTM-ReLU model outperforms LsTM-softmax, Naïve bayes and KNN method. LsTM-ReLU achieves an average performance gain of 40% - 46% over LsTM-softmax imputation with varied missingness value and 40% - 42% on varied hyper-parameter size. Accuracy is much greater and mean square error less than 0.02% with Naïve bayes and KNN model. © 2021 IEEE.

关键词： Long short-term memory

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Emotion based Music Recommendation system for specially-Abled 6

Emotion Based Music Recommendation System for Specially-Able...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： shreya, L. Nagarathna, N. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665435598

Human emotion is a psycho physiological state of mind that manifests as good or negative responses to external and internal stimuli. songs have long been a popular alternative for expressing human feelings as a medium of expression. Content-based recommendation engines are used by the majority of existing music recommendation systems. However, music selection is influenced not only by past tastes or musical substance, but also by the user's current mood. This research presents a framework for learning a user's emotion using data gathered from a wearable device that is combined with galvanic skin response (GsR), photo plethysmography (PPG), and electro encephalography (EEG) physiological sensor signals, as well as data taken via camera. This information is used as a supplement to the music recommendation engine. sensor data and face expression data can thus improve the effectiveness and accuracy of the recommendation engine. © 2021 IEEE.

关键词： Emotion Recognition

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