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Breast Cancer Prediction in machine learning Techniques using Blockchain 6

Breast Cancer Prediction in Machine Learning Techniques usin...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Anand, R. Aneeshsa, D Manisha, S. thanuja, R.K.V KCG College Technology Department Of Information Technology Chennai India

ISBN: (纸本)9781665425520

Breast cancer (BC) is one all around the huge average dangerous enhancements among women around the globe, watching out for by a long shot the greater part of latest disease cases and harm related passings per all things considered evaluations, making it a genuine public awfulness in the flow society. Lately, AI has been thoroughly used in locale and achieved positive execution. Our exploration gives a absolute manual for affectability appraisal of model cutoff focuses concerning execution in district of carcinoma organizes by anticipating end inside the condition of dataset credits. BC is that the by and large standard frightful headway among women besides the subsequent driving clarification for defilement passing. Early finding of underhandedness is major. the model got by an intrusive methodology is in like manner acceptably digitized and used for computationally based reasonable. Using AI systems for resolute can on a very basic level revive and for a goliath increase can make the fascinating everything thought about more moderate. the assessment of dataset by guiltless baysien check to a few information looks like, variable ID, uni-variate evaluation, bi-variate and multi-variate examination, missing worth solutions, etc the key goal is to sharp appraisal model to dismantle carcinoma seasons of patients. Moreover, talk about the introduction from the given clinical office dataset with evaluation request report and see the disorder network. the data endorsing, data cleaning/planning and data portrayal are finished with everything considered given dataset. Henceforth, planned AI appraisal want to engineering data from need and therefore the outcome shows that the authority of the forthcoming AI rejects activity is stood and best exactness from accuracy, anamnesis and F1grade © 2021 IEEE.

关键词： Python

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Formulation of Sensor Ranking Associated in Categorical Perception: A Pilot Study Using machine learning 6th

Formulation of Sensor Ranking Associated in Categorical Perc...

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6th Kuala Lumpur international conference on Biomedical Engineering, BioMed 2021

作者： Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9783030907235

the concept of categorical perception has been enormously investigated to digitalize the process of auditory processing when perceiving speech stimulus at higher brain neurological signal. Despite the nature non-stationary property of electroencephalography (EEG) during any task processing, scientists and clinicians find it to be not well fitted in the healthcare setting application without professional monitoring, and even worst, the hearing aid device functionality was also reported at a low success rate. In this study, we embraced machine learning technology and extracting contribution into our auditory research area. the present pilot work aims to create a robust computational framework to formulate the sensor ranking principle in auditory speech perception. the ranking for sensors could facilitate in identifying the minimal set sensor-of-interest (ROI) that are sufficient in specific auditory task processing using an optimally trained model. the trained Support Vector machine (SVM) highest performance reported at random 2 training dataset with scoring of 92.3% using 70% triple-random training dataset. Based on the sensor ranking, the CZ electrode outperformed the other electrodes with scoring of 96.74%, followed by PZ and FPZ for the 2nd and 3rd rank (95.66% and 95.34% respectively). Our pilot study anticipated that the sensor ranking formula able to underline more precise neural correlates based on current auditory categorical perception response. the excellence sensor ranking in delivering a minimal set of sensor-of-interest (SOI) drive the capability of the SVM model in classifying auditory brain response in high-performance prediction metrics and possible reliability in the healthcare setting application. © 2022, Springer Nature Switzerland AG.

关键词： Support vector machines

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Implementation of machine learning Techniques for Predicting Traffic Flow in Smart Cities

Implementation of Machine Learning Techniques for Predicting...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Vishal Goyal Madhuri Bore Yatika Gori K Mayuri A L N Rao Om Krishna Electronics & Communication Engineering GLA University Mathura Uttar Pradesh India Department of Civil Engineering Indira College of Engineering and Management Pune Maharashtra India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India Lloyd Law College Greater Noida Uttar Pradesh India

Computers use machine learning, which is a collection of calculations and quantifiable models, to carry out anticipated tasks. Face recognition, conversation recognition, clinical determination, quantifiable exchange, traffic forecasting, and more tasks can be accomplished using machine learning. With the aid of central traffic-monitoring servers, GPS route has become widely used in recent years in determining traffic proportion in large cities. A concept illustrating the city's current traffic could be developed using the information acquired, and it could be used in the future to make traffic forecasts and allow for a clog analysis. thus, forecasting traffic flow is the only emphasis of this endeavour. Two open data sets are used to prepare, approve, and test the proposed ML and DL models. the first contains the total number of vehicles counted at 6 intersections using various sensors over 56 days. the ML and DL models for this study are built using four of the six convergences. Slope Supporting, Intermittent Brain Organizations (RNNs), and Arbitrary Woods, Direct Relapse, and Stochastic Angle followed closely by the Multi-facet Perceptron Brain Organization (MLP-NN), which achieved better results while requiring less preparation time. All ML and DL calculations had excellent execution measurements, demonstrating their viability for use with intelligent traffic signal regulators.

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SmartEDU: A Platform for Generating Education-Support Materials

SmartEDU: A Platform for Generating Education-Support Materi...

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Experiment@ international conference (exp.at)

作者： Maria João Costa Renato Matos Hugo Amaro Bruno Caceiro Alcides Marques Hugo Gonçalo Oliveira Instituto Pedro Nunes LIS Portugal CISUC DEI University of Coimbra Portugal Mindflow Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

With technology taking a more prevalent role in our daily activities, new opportunities and challenges emerge. New technological tools have been successfully used in the educational context for some time now, facilitating teachers, educators, and trainers in the transmission of knowledge. However, there are still tasks that can take advantage of these developments, as is the case with the creation of multiple-choice questions and slide presentations. Artificial Intelligence, in general, and machine learning and Natural Language processing, in particular, can be used to automatically generate initial versions of slide decks and of natural language questions. Instead of creating them from scratch, teachers and trainers will only have to perform minor editions, and save time for other tasks. this document presents the platform SmartEDU, which allows users to generate multiple-choice questions and drafts of slide presentations from any text.

关键词： machine learning User interfaces Natural language processing Task analysis

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Halloween Structured Microstrip MIMO Radiator at 5G sub-6GHz and mm-wave Frequencies

Halloween Structured Microstrip MIMO Radiator at 5G sub-6GHz...

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international conference on the Paradigm Shifts in Communication, Embedded Systems, machine learning and signal processing (PCEMS)

作者： K. Naga Raju A. Kavitha Ch Sekara Rao Kaitepalli Department of Information and Communication Engineering Anna University Chennai Tamil Nadu India Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering karur Tamilnadu India

the 5th generation mobile technology standards have the guts to deliver high data rates, low latency communications, and massive device connectivity. One of the challenge in front of 5G was spectrum usage, this article was presented, different frequency bands allocated to 5G in the range of sub-6GHz and millimeter wave frequencies in various countries and for the efficient distribution of the 5G systems and in order to meet the challenge of spectrum usage, developed a dual wideband three elements MIMO radiating structure capable to operate in sub-6GHz and mm-wave bands. Primarily the single element radiator was designed on 6.4 X 6.4 X 0.25 mm 3 dimensioned Rogers/RT duroid substrate with Halloween structure incorporated patch and grounded with wide rectangular slot. the incorporation of Halloween structure was given gain enhancement and wide rectangular slot on ground was given bandwidth enhancement. the single element radiator was resonated in the band 25.18-29.39GHz with the gain of 4. 69dBi. the MIMO antenna structure was developed by extending the dimensions to S.4 X 23. SX0.25mm 3 , it was capable to operate in dual bands 5.12-5.97GHz and 12.83-40.4GHz with respect to the -10dB return losses and less than -10dB isolation was observed with respect to adjacent radiators. the Maximum gain obtained over the lower band was 6. 7dBi and higher band was 17. 36dBi. the results demonstrated that the presented antenna structure is well suitable for 5G Applications.

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Application of machine learning and Blockchain Technology in Improving Supply Chain Financial Risk Management

Application of Machine Learning and Blockchain Technology in...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Anas Ahmad Bani Atta Ahmad Yahiya Ahmad Bani Ahmad Mahmoud Izzat Allahham Dharini Raje Sisodia Rupesh Roshan Singh Umme Habiba Maginmani Financial and Accounting Science Department Financial Technology Faculty of Business Middle East University Amman Jordan Financial and Accounting Science Department Middle East University Amman Jordan College of Business LTUC Jordan Management Army Institute of Management and Technology Block-14Mittal School of Business Lovely Professional University Phagwara Punjab India Department of Computer Science & Engineering Secab Institute of Engineering and Technology Vijayapur Karnataka

the database used by blockchain innovation is run by numerous components and creates a network pattern using hash indexes. the blockchain employs numerous nodes and transmits numerous data access points, decreasing reliance on the primary Internet server and preventing the danger of data destruction and server breakdown. the data files saved in the blockchain are protected using encryption techniques to maintain their authenticity and prevent illegally added or discarded data files. With technological characteristics like non-tampering, distributed ledger, and reliability, blockchain innovations and machine learning have innate benefits in supply chain finance. they also have great prospects to construct faith in order to overcome the major issues in supply chain finance, which is helpful for fostering economic advancement in the Tonkin Gulf region. this paper focuses on introducing the study on using blockchain innovation in supply chain finance in the Tonkin Gulf region and aims to offer suggestions on how supply chain finance could evolve there using blockchain innovation. this paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the blockchain and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region. the empirical findings presented in this paper demonstrate that the built blockchain supply chain finance platform has an algorithm with an overall processing time of 4.10 seconds, a computational efficiency that is quicker, and the ability to more accurately analyse the pertinent concerns.

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Detection of Sarcasm on Amazon Product Reviews using machine learning Algorithms under Sentiment Analysis 6

Detection of Sarcasm on Amazon Product Reviews using Machine...

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6th international conference on Wireless Communications, signal processing and Networking (IEEE WiSPNET)

作者： Rao, Mandala Vishal Sindhu, C. Consultant Larsen & Toubro Infotech Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665440868

Most of the people express their ideas, views and opinions over social media. these feedbacks or comments carry an emotion in them. this data can be either straight forward or sarcastic. this text has to be analyzed and make the reviewers understand the exact intent of the writer. Sentiment analysis is used to analyze the perspective of text. Sarcasm can also be present in the text which is a bitter way of conveying the information. Selection of the dataset is the initial task. Dataset is retrieved from Amazon datasets. the next task is preprocessing of data which includes tokenization of data, polarity identification, stemming and lemmatization. Later, feature extraction is done, which includes term frequency, Inverse document frequency and n-gram. the classification algorithms are used such as Support Vector machine (SVM), K Nearest Neighbors and Random forest are implemented. Further the calculation of results is evaluated by the parameter accuracy.

关键词： Sarcasm Irony Support Vector machine Satire Naive Bayes Random forest

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Everyday Life Tremor signal processing in PD Patients using BSN 6

Everyday Life Tremor Signal Processing in PD Patients using ...

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6th international conference on Advances in Biomedical Engineering (ICABME)

作者： Babayan, Joseph Lueken, Markus Berking, Arun Pickartz, Anne Reetz, Kathrin Holtbernd, Florian Leonhardt, Steffen Chuong Ngo Rhein Westfal TH Aachen Med Informat Technol Aachen Germany Rhein Westfal TH Aachen Uniklin Med Hosp Aachen Germany Juelich Res Ctr Inst Neurosci & Med Julich Germany

ISBN: (纸本)9781665435017

Parkinson's disease is a neurological disorder characterized by the deficiency of dopamine levels in the brain. More than 75 percent of these patients suffer from tremors. Parkinsonian tremor (PT) is more characterized to be a rest tremor, but some patients suffer from action tremor as well. Usually, patients suffering from this disease are diagnosed by their physicians who perform some battery MDS-UPDRS tasks to determine the disorder. Some sensors were used to diagnose the tremor objectively, but in this study, we are using a new Body Sensor Network (BSN) designed at our institute to be used in detecting the acceleration, gyroscope, and magnetometer of the tremor patients in the clinic. signal processing of the recorded data is performed to determine and classify the number of times throughout the day the patient suffered from tremors. this is ensured through automatic signal segmentation, extraction of several signal features, and classification with the most accurate machine learning classifier. In this study, we have proved that our BSN sensor is capable of helping clinicians in classifying tremor occurrence in Parkinson diseased patients specifically, and tremor patients generally throughout monitoring their everyday life activities.

关键词： Parkinson's disease (PD) Body Sensor Network (BSN) tremor automatic segmentation feature extraction classification

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Wearable Sensors-based Activity Classification for Intelligent Healthcare Monitoring

Wearable Sensors-based Activity Classification for Intellige...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Fakhra Nazar Ahmad Jalal Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Evaluation of physical activity is relevant for the advancement of the nation’s health, as it has been evidenced about the connection between physical activities, obesity, metabolic syndromes, and many other health-related conditions. this evolution has led to the creation of multiple kinds of functional datasets that allow research in a variety of fields. In this work, we showcased a HAR mechanism that distinguishes 12 forms of physical activities based on data coming from inertial sensors - accelerometers and gyroscopes. this work adopts an intelligent healthcare system where statistical features are deployed from the sensor data, optimized via the Ant Colony Optimizer, and classifies HAR via MLP. this system is able to identify activities of daily living affordably with high accuracy and efficiency. then we also checked out our results comparatively with some other machine learning models, and found that our suggested model has a high accuracy of 93% than other models. these findings support the fact that, indeed, the given framework can be employed for the assessment of human activities and segregation between healthy and unhealthy clients.

关键词： Accelerometers Obesity Accuracy Medical services Computer architecture signal processing Feature extraction Gyroscopes Human activity recognition Monitoring

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Multi-Layer Visual Perception for No-Reference Image Quality Assessment

Multi-Layer Visual Perception for No-Reference Image Quality...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Junwei Qi Yingzhen Wang Qingchun Wang Yingsong Li College of Computer Science and Technology Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results.

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