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QoS parameters for Comparison and Performance Evaluation of Reactive protocols 1

QoS parameters for Comparison and Performance Evaluation of ...

1st IEEE International Conference on Computational Intelligence for Smart Power System and Sustainable Energy, CISPSSE 2020

作者： Latchoumi, T.P. Vasanth, A. Vijay Bhavya, Battula Viswanadapalli, Anusha Jayanthiladevi, A. Vfstr Deemed to Be University Department of Cse Guntur India VelTech Rangarajan Sagunthala RandD Institute of Science and Technology Department of Cse Chennai India Vfstr Deemed to Be University Guntur India Professor-Computer Science and Information Science Srinivas University Karnataka India

ISBN: (数字)9781728172743

ISBN: (纸本)9781728172743

Reputation Aware Multi-hop Routing Protocol (RAMP) is an efficient routing algorithm for the effective congestion control in Mobile Adhoc Network (MANET) and it falls under the category of Reactive protocols. The RAMP was introduced to assign a trusted value to a node and from which the node connection is established between nodes. In this paper, RAMP has been evaluated in different criteria, namely Packet delivery ratio, packet drop, energy consumption, end delays and routing overhead. The Quality of Service (QoS) executes a major task in shaping the working of a routing algorithm in MANET. In RAMP, all the nodes are uniform, effective and cooperative. So, it is necessary to evaluate the RAMP under QoS parameters. © 2020 IEEE.

关键词： Quality of service

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Assessing quality of decision reducts

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Procedia computer science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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Reduct-based ranking of attributes

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Procedia computer science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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Automatic Detection of Infection in Diabetic Foot Ulcer Images Using Improved-CNN-SVM Approach

Automatic Detection of Infection in Diabetic Foot Ulcer Imag...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Sivashankar. G Bhagyalakshmi Avinash Mohammad Shabbir Alam Manasi Vyankatesh Ghamande Keerat Kumar Gupta Ravi Rastogi Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamilnadu India Dept of Orthodontics & Dentofacial Orthopaedics JSS Dental College & Mysore Mysore Karnataka India Department of Computer Science College of CS &IT Jazan University Jizan Kingdom of Saudi Arabia Assistant Professor E and AS Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Scientist D Electronics Division NIELIT Gorakhpur Uttar Pradesh India

Unregulated glucose levels in the blood lead to the chronic disease diabetes. Diabetic foot ulcers (DFUs) and other devastating outcomes may be avoided with early detection. The lower limb of a diabetic patient may need to be amputated if they experience a DFU. DFU is difficult to diagnose and usually requires a number of expensive and time-consuming clinical investigations for the treating physician. Applying deep learning, machine learning, and computer vision techniques in today's age of data deluge has resulted in a number of solutions that can help doctors make more accurate diagnoses in less time. As a result, researchers have recently focused more on developing methods for automatically identifying DFU. Preprocessing, segmentation, feature extraction, and model training are all used in the suggested method. It does noise reduction and RGB to HSI color space conversion during preprocessing. OSTU thresholding segmentation is used for the separation. It uses histogram for feature extraction and Improved CNN-SVM for model training. The new method is compared to two common approaches, including CNN and CNN-SVM, and fares better than both.

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Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four Switch Induction Motor Drive

Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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Explainable AI to Analyze Outcomes of Spike Neural Network in Covid-19 Chest X-rays

Explainable AI to Analyze Outcomes of Spike Neural Network i...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Md. Sarwar Kamal Linkon Chowdhury Nilanjan Dey Simon James Fong Kc Santosh School of Computer Science FEIT University of Technology Sydney Sydney Australia East Delta University Chattagram Bangladesh Associate Professor Computer Science JIS University Kolkata India Kolkata West Bengal India University of Macau Taipa Macau SAR The University of South Dakota Vermillion South Dakota

ISBN: (纸本)9781665442084

Analysis of irregularities in Covid-19 data could open a new window to learn more about the unprecedented problems of the current global pandemic. Of many, radiographs and clinical records are reliable sources for viral infection investigation and treatment planning. Clinical records help track the Covid-19 pandemic. In this paper, we present a Spike Neural Network (SNN) with supervised synaptic learning to detect abnormalities in Chest X-rays (CXRs) In other words, the proposed SNN can distinguish Covid-19 positive cases from healthy ones. In our decision-making procedure, we introduce clinical practice so Explainable AI (XAI) is possible to carry out. In addition, Support Vector Machine (SVM) with local interpretable model-agnostic explanation (LIME) provides reliable analysis of abnormalities in Covid-19 clinical data.

关键词： COVID-19 Support vector machines Radiography Pandemics Neural networks Planning Reliability

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Unsupervised Machine Learning Medical Image Registration using Recurrent Deformed Pyramids Networks

Unsupervised Machine Learning Medical Image Registration usi...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： R Thanga Kumar S.K Sunori K. Yuvaraj Mridula Gupta Bhandari Mahesh Ashok S. Sathiya Naveena School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka Graphic Era Hill University Bhimtal Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India IT Vishwakarma Institute of Technology Pune India Department of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

For many therapeutic uses, including image-guided therapies, picture registration is crucial in medicine. Though challenging, this field of medical picture registration has lately seen significant algorithmic performance improvements because to the rise of machine learning. Some medical applications are made possible by deep neural networks, such as faster and more accurate picture registration, which is crucial in the fight against malignancies during surgery. Medical image registration procedures sometimes include complex distortion issues. Despite the several suggested sophisticated registration models, deformable registration continues to be a tough task, particularly when dealing with large volumetric deformations, in terms of accuracy and efficiency. Our unsupervised pyramid attention networks (PAN) and network are designed for non-rigid and flexible registration, respectively, with this goal in mind. In contrast to networks that depend on high-weight attentions or transformers, our network is a clean convolutional pyramid, making full use of the pyramid structure's inherent benefits. Specifically, our network guarantees the deformation field's rationality by predicting it from coarse to fine using a step-by-step recursion method that incorporates high-level semantics. Our network is able to achieve deformable registration without the need for separate affine pre-alignment because of the recursive pyramid technique. In terms of Dice scores, average symmetric surface separation, Hausdorff distance, and Jacobian, our network constantly outperforms state-of-the-art, according to experimental data. Our network continues to perform well in adjusting for the huge deformation even when the affine pre-alignment is not included in the data.

关键词： Training Image registration Accuracy Deformation Semantics Surgery Machine learning Transformers Vectors Biomedical imaging

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An Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop 1

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2nd International Conference on Soft Computing and Signal Processing, ICSCSP 2019

作者： Reddy, P. Chandra Shaker Sureshbabu, A. Research Scholar JNTUA AnanathapuramuAP India Department of Computer Science & Engineering CMR College of Engineering & Technology HyderabadTS India Professor of CSE Department JNTUA College of Engineering AnanathapuramuAP India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

Rice is an essential yield amongst the most essential food crops of India and it is grown everywhere throughout the nation. Rice is a major yield in the semi-arid district as Ananthapur is the part of Andhra Pradesh (AP) state in India. Precise and early forecasting of rice yield can give valuable information to inside season alteration of yield management. Time series data has been of incredible significance to investigate the area of forecasting strategies and times series models are used for rice yield forecasting from various investigators across the world, yet the forecast has not been precise. In this investigation, we proposed an effective approach to predict seasonal rice production of coming four years based on existing data. The model was build based on rice production data, which it is collected from agricultural department Andhra Pradesh. Rice production data of two seasons (Kharif and Rabi) was gathered in the period of 2008–2014 from Ananthapur district. In this article, we introduced Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model for prediction for rice crop production for next four years seasonalwise and give more precise outcomes than the previous existing models. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Interactive visual labelling versus active learning:an experimental comparison

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Frontiers of Information technology & Electronic Engineering 2020年第4期21卷 524-535页

作者： Mohammad CHEGINI Jurgen BERNARD Jian CUI Fatemeh CHEGINI Alexei SOURIN Keith ANDREWS Tobias SCHRECK Institute of Computer Graphics and Knowledge Visualisation Graz University of TechnologyGraz 8010Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore InfoVis Group University of British ColumbiaVancouver V6T1Z4Canada Max Planck Institute for Meteorology Hamburg 20146Germany Institute of Interactive Systems and Data Science Graz University of TechnologyGraz 8010Austria

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data *** quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the *** instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming *** learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning *** visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a *** putting the analyst in the loop,higher accuracy can be achieved in the resulting *** initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely *** paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate *** results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for *** also employ different labelling strategies depending on the visualisation used.

关键词： Interactive visual labelling Active learning Visual analytics

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The Estimation of PR Method with the use of LV of Web Pages

The Estimation of PR Method with the use of LV of Web Pages

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： G Tarun Vikrant Sharma Mustafa Mudhafar Vipluv Pathak Qasim Abdul Jabbar Adil Abbas Alwan Laith H. Alzubaidi Ali Saad Alwan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand Department of Anesthesia Techniques and Intensive Care Al-Taff university college Kerbala Iraq Department of Management Studies GL Bajaj Institute of Technology and Management Greater Noida India Medical Instrument Department Alkunooze University College Basra Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Business Administration Department Kut University College Al kut wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The fast expansion of the World Wide Web has made it increasingly harder to navigate through its tremendous content, necessitating green and effective internet net page rating algorithms for serps like google. Traditional rating algorithms, which incorporates PageRank, HITS, and Weighted PageRank, drastically talking rent hyperlink-primarily based metrics to decide the relevance and importance of internet pages. However, those algorithms frequently fail to include person engagement statistics, which can provide important insights into the real software program of net pages. This paper introduces a completely unique approach to web page ranking-the Link-Visit PageRank (LVP) set of rules-which enhances traditional models through way of incorporating the frequency of link visits. By integrating how often customers definitely click on on links, the LVP algorithm targets to supply greater correct and customer-centric web page scores. This approach now not only ensures to decorate the relevance of seek engine consequences but additionally aligns with the dynamic and consumer-pushed nature of contemporary web navigation.

关键词： Measurement Navigation Heuristic algorithms Software algorithms Web pages Estimation Software

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