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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： K. Ananthi M. Balakrishnan M. Pandi N. Senthil Madasamy Artificial Intelligence &Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library.

关键词： Measurement Fuzzy logic Weight measurement Training Computer bugs Support vector machine classification machine learning

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A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field

A Deep Assessment of ML Based Procedure used as a Classifier...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Kuldeep Sharma A Madeswaran Awadhesh Chandramauli V Narasimha Laith Jasim Y. Mary Reeja Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India School of Business and Management CHRIST (Deemed to be University) Bangalore Yeshwanthpur campus Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE CMR College of Engineering & Technology The Islamic university Najaf Iraq Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. this research paper introduces a unique machine learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Naïve Bayes, Support Vector machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. this method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than.

关键词： Support vector machines machine learning algorithms Computational modeling Smart healthcare Neural networks machine learning Predictive models

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An Investigation into Big data of Emergency Rescue based on an Improved DDRfs

An Investigation into Big Data of Emergency Rescue based on ...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Jiabin Deng Juanli Hu School of Information Engineering Zhongshan Polytechnic Zhongshan City Guangdong Province China

Post-validation data anomaly still exists in emergency rescue operations, such as spelling mistakes, abbreviated or shortened data formats during the pre-processing and data integration stages. If un-corrected or un-identified, resulting in a drop in data accuracy as the quality of data would be severely affected while performing data analysis or mining. In this essay, the authors propose a new methodology. We mixed the Fuzzy C-means Clustering Algorithm with Levenshtein Distance Method and created a new approach called De-duplicated Record for Similarity (DDRfS) to increase fuzzy search accuracy. Based on our tests, calculations in the data partition stage generated a much higher matching accuracy.

关键词： machine learning algorithms Clustering algorithms data integration machine learning Big data Partitioning algorithms data mining

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Attention-Driven Body Pose Encoding for Human Activity recognition 25

Attention-Driven Body Pose Encoding for Human Activity Recog...

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25th international conference on pattern recognition (ICPR)

作者： Debnath, Bappaditya O'Brien, Mary Kumar, Swagat Behera, Ardhendu Edge Hill Univ Dept Comp Sci Ormskirk L39 4QP England Edge Hill Univ Fac Hlth Social Care & Med Ormskirk L39 4QP England

ISBN: (纸本)9781728188089

this article proposes a novel attention-based body pose encoding for human activity recognition. Most of the existing human activity recognition approaches based on 3D pose data often enrich the input data using additional handcrafted representations such as velocity, super-normal vectors, pairwise relations, and so on. the enriched data complements the 3D body joint position data and improves model performance. In this paper, we propose a novel approach that learns enhanced feature representations from a given sequence of 3D body joints. To achieve this encoding, the approach exploits two body pose streams: 1) a spatial stream which encodes the spatial relationship between various body joints at each time point to learn spatial structure involving the spatial distribution of different body joints 2) a temporal stream that learns the temporal variation of individual body joints over the entire sequence duration to present a temporally enhanced representation. Afterwards, these two pose streams are fused with a multi-head attention mechanism. We also capture the contextual information from the RGB video stream using a deep Convolutional Neural Network (CNN) model combined with a multi-head attention and a bidirectional Long Short-Term Memory (LSTM) network. Finally, the RGB video stream is combined with the fused body pose stream to give a novel end-to-end deep model for effective human activity recognition. the proposed model is evaluated on three datasets including the challenging NTU-RGBD dataset and achieves state-of-the-art results.

关键词： Long short-term memory

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Customs Fraud Detection Method Based on Artificial Intelligence and data Analytics

Customs Fraud Detection Method Based on Artificial Intellige...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Veska Gancheva George Popov Ognyan Nakov Kamelia Raynova Antoaneta Popova dept. of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria dept. of Cybersecurity Technical University of Sofia Sofia Bulgaria dept. of Computer Systems Technical University of Sofia Sofia Bulgaria dept. of Information Technologies in Industry Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Because of the increased market opening and massive data collection brought about by globalization, the need of maintaining control over customs procedures has increased. However, the integration and processing of customs data are complicated by its extreme imbalance. Finding autonomous, computationally sophisticated solutions for customs handling is therefore crucial. the study described in this paper aims to propose an artificial intelligence-based method for customs fraud detection through data analytics. A machine learning-based approach for detecting customs fraud is proposed. the research methodology includes data preprocessing, data analytics, combining results, and deriving precise solutions from results obtained. the aim is to extract meaningful information from a dataset. For the purpose of classifying customs fraud, seven algorithms for machine-learning-kNN, SGD, Constant, Logistic Regression, Random Forest, Neural Network, and Naive Bayes-are chosen and empirically assessed.

关键词： Training Analytical models Logistic regression data analysis Neural networks data models Fraud Bayes methods data mining Random forests

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Applying Social Network Analysis and data mining Techniques to Support Decision-Making: A Case Study 4

Applying Social Network Analysis and Data Mining Techniques ...

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4th international conference on machine learning and Intelligent Systems, MLIS 2022

作者： Freire, Manuela Antunes, Francisco Costa, João Paulo CeBER Faculty of Economics Av Dias da Silva 165 Coimbra3004-512 Portugal Department of Management and Economics Beira Interior University Portugal INESCC-Computer and Systems Engineering Institute of Coimbra Portugal

ISBN: (纸本)9781643683560

the key goal of this work is to explore interactions and discursive exchanges between social users, to extract information towards decision support. We analyzed customer-generated data on Facebook, during a period of a ten-day strike, of a well-known airline company. the main goal was to check service and responsiveness of the airline, and also to develop indicators that might enable reviewing and reinforce strategies to be used in customer service response to strike events. the authors aim to investigate the possibility of structuring data, collected from OSN's, incorporating human interaction and network structure, using SNA to study the network from a duo fold manner: the web discourse, which depends on the transmission of information;and the interaction among social users, as information disseminators. Our work intends to determine whether social users and their interactions are consistent with the creation of indicators for decision support. © 2022 the authors and IOS Press.

关键词： data mining

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theoretical Evaluation of Transfer learning Approaches for the Identification of Lung Respiratory Sounds

Theoretical Evaluation of Transfer Learning Approaches for t...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Prarthna Dubey Paritosh Tripathi Department of Information and Technology Institute Of Engineering And Technology Dr. Rammanohar Lohia Avadh University Ayodhya U.P. India

machine learning approaches for lung respiratory sound identification and categorization have increased in study. Transfer learning uses pre-trained models to extract relevant attributes from input data, one of the most promising strategies for high accuracy in this activity. In this review, many transfer learning algorithms for lung breathing sound identification are described. First, discuss respiratory sounds and their clinical significance. Next discuss the challenges of recognizing these sounds, such as the vast variety of sound characteristics and the requirement for trustworthy feature extraction techniques. Next, examine transfer learning methods such fine-tuning pre-trained convolutional neural networks (CNNs), learning from comparable tasks, and unsupervised pre-training. Focusing on current research on lung respiratory sound identification. Also compare each method’s benefits and limitations. the discussion finishes with the field’s most urgent difficulties, including the need for bigger and more diversified datasets, enhanced feature extraction techniques, and clinical information in machine learning models. In conclusion, this research examines the latest transfer learning algorithms for lung breathing sound recognition. It emphasizes these technologies potential to improve clinical diagnostic accuracy and efficiency and recommends numerous interesting research opportunities.

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Local Activation Identification in Persistent Atrial Fibrillation Intracardiac EGM Signals for Automatic Spatio-temporal Dispersion pattern recognition 46

Local Activation Identification in Persistent Atrial Fibrill...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Frusone, Sara De Almeida, Rafael Costa Squara, Fabien Zarzoso, Vicente Université Côte d'Azur Cnrs I3S Laboratory Sophia Antipolis France Federal Institute of Espírito Santo Vitoria Brazil Université Côte d'Azur Pasteur Hospital Cardiology Department Nice France

ISBN: (纸本)9798350371499

Atrial fibrillation (AF) is a common cardiac condition that predominantly affects the elderly population, presenting a significant risk factor for strokes and thus raising concerns in public health. Catheter ablation (CA) stands out as the most effective long-term treatment for persistent AF. A recently proposed novel CA approach is based on spatio-temporal dispersion (STD). this technique targets the STD patterns associated with active zones responsible for sustaining the arrhythmia. In this work we want to solve the peak detection problem, since it is a fundamental step for the automatic classification of STD patterns from multipolar electrograms (EGM). Instead of using machine learning models which lacks explainability, we want to understand the classification process performed at the block by interventional cardiologists in real time. the scenario is very challenging because the STD classification relies on visual peak detection to identify local activations, which are used to measure if STD does occur or not. We present our peak detector comparing it with eight different techniques from the state of the art. To extract peaks from real intracardiac EGM signals is difficult, most classical signal processing methods fail. We evaluate a total of nine techniques on the challenging scenario of real STD data. We analyze if the peaks are correctly identified, being part of the mathematical pipeline. Results show that identifying the peaks is a fundamental aspect to built the presented mathematical pipeline to overcome the STD classification problem, improving the classification accuracy with respect to previous works. © 2024 IEEE.

关键词： Ablation

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AI-Infused Research and Development in Universities: Accelerating Scientific Discovery

AI-Infused Research and Development in Universities: Acceler...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Badrus Moch. Abdulloh Salim Ika Novita Sari Septiana Azha Moh. Zulfikri Sutantri Tribakti Islamic University Lirboyo Kediri Indonesia Indonesia

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

the past decade has witnessed the role that artificial intelligence (AI) has in university research and development (R&D) processes emerge as a very potent catalyst for accelerating scientific discovery. It thus seeks to explore the transformative impacts that AI-infused R&D is likely to bring in universities, focusing mostly on key mechanisms, challenges, promising avenues for future exploration, and technological breakthroughs in the field. the traditional methodologies of R&D have seen a massive revolution through the entry of AI, which now poses uncharted capabilities in data analysis, pattern recognition, and predictive modeling. the application is able to thoroughly search vast sets of data in an unrealistically short period, so as to reveal latent structures, patterns, and other actionable insights, with the help of machine learning algorithms, neural networks, and natural language processing techniques. this paradigm shift in R&D supports a more agile, data-driven approach to scientific inquiry that allows researchers to better focus their efforts with precision and alacrity on the most vexing, complex problems. the biggest gain to be reaped from infusing $A I$ in R&D is the hastening of the pace of experimentation and hypothesis testing. AI systems thus enable researchers to iterate very quickly by automating repetitive tasks and offering the best designs for experimentation. this will, in effect, bring down the time-todiscovery cycle. Equally, AI simulations allow for the possibility of in silico virtual experiments, thus reducing overreliance on capital-intensive and unwieldy laboratory experiments.

关键词： Training Watersheds Predictive models Trajectory pattern recognition Artificial intelligence Task analysis

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Training set AERIAL SURVEY for data recognition Systems From Aerial Surveillance Cameras 4

Training set AERIAL SURVEY for Data Recognition Systems From...

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4th international Scientific conference "Information Technology and Implementation", IT and I 2022

作者： Zivakin, Valery Kozachuk, Oleksandr Prystavka, Pylyp Cholyshkina, Olha National Aviation University Liubomyra Huzara ave. 1 Kyiv03058 Ukraine Interregional Academy of Personnel Management Frometivska St. 2 Kyiv03039 Ukraine

recognition of terrain elements and objects from aerial image data wich obtained from aircraft air surveillance cameras, UAVs and high-resolution satellite images is a complex task and requires numerical factors to be taken into account. the created training set for intelligent systems AERIAL SURVEY allows solving a wide range of scientific and practical problems in the field of machine learning and neural network recognition of terrain elements. the purpose of this paper is to show the dynamics of the conducted research on the creation of the training set AERIAL SURVEY, identify problem areas and identify promising vectors for further development of this area. the training set already now allows solving a number of video analytics tasks, including dual-use ones: various aerial monitoring of roads, agricultural fields, forests, water bodies, aerial reconnaissance with the search for targets in the combat zone, use in tracking and target designation systems. In the course of further research, the aerial survey data set will allow solving such problems as information filtering of excessive information content of terrain images, automated formalization and description of new unlabeled classes, the transition from recognition of individual terrain elements to a structural description of the aerial survey area in order to automate its georeferencing and many others. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Intelligent systems

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