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Optimal-MDPREF: Novel approach for distributive association rules mining

Optimal-MDPREF: Novel approach for distributive association ...

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international conference on intelligent Systems: theories and Applications (SITA)

作者： Mouhir Mohammed Balouki Youssef Gadi Taoufiq Dept. of Mathematics and Computer Sciences FSTS University of Hassan Settat Morocco

ISBN: (纸本)9781509057825

Association Rules is applied on databases - small or big - as a technique to discover useful knowledge with regard to predefined conditions, it is in the core of data mining and is also used in several fields of business such as insurance, medicine, education.... the algorithms which are used to mine Association Rules, usually generate a huge number of rules. this fact makes them beyond the user's ability to interpret. Prior to this is that the generation process may not in most cases be fast enough to save time and effort. the objective of this contribution empowered by MDPref algorithm is to lessen the number of rules and to make the generation process relatively faster via scaling-distributive approach to make its efficiency better and the runtime shorter.

关键词： data mining Machine learning algorithms Partitioning algorithms databases Algorithm design and analysis Runtime Classification algorithms

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Cognitive Fatigue Detection in Vehicular Drivers Using K-Means Algorithm 2nd

Cognitive Fatigue Detection in Vehicular Drivers Using K-Mea...

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2nd international conference on Innovative Computing and Communication, ICICC 2019

作者： Sharma, Manish Kumar Bundele, Mahesh Computer Engineering Poornima College of Engineering JaipurRajasthan India

ISBN: (纸本)9789811503238

In vehicular drivers, the cognitive fatigue has been one of the major factors that leads to loss of lives or disabilities due to vehicular accidents (Bundele and Banerjee in Proceedings of the 11th international conference on information integration and web-based applications and services, ACM, pp 739–744, 2009 [1]). the factors governing driver fatigue such as monotonous driving, traffic conditions on road, road conditions, insufficient sleep, anxiety, health conditions, work environment, type of vehicle, and driving comfort do affect largely the driving behavior. Many researchers across the world are working finding best suitable methods to minimize vehicular accidents. In this paper, our proposed approach is an alternative solution to detect cognitive fatigue in vehicular drivers to decrease the number of incidence of vehicular accidents specially those occurred due to cognitive fatigue (Hu and Zheng in Expert Syst Appl 36:7651–7658, 2009 [2]). the objective of this work has been to provide simple classification technique using K-means algorithms. the basic K-means and two modified versions have been proposed and validated to reliably detect cognitive fatigue while driving. It is of paramount importance that the sensory parameters are chosen such that they could be used without causing discomfort to the driver and without creating obstruction while driving. the data for simple physiological signals such as skin conductance (SC), oximetry pulse (OP), and respiration (RSP) for pre- and post-driving state of drivers has been used for sensing change in fatigue level of the drivers (Bundele and Banerjee in 2009 second international conference on emerging trends in engineering & technology. IEEE, pp 934–939, 2009 [3]). All features of statistical and wavelet were extracted and analyzed (Brown in D1 Methodological issues in driver fatigue research. Fatigue and driving: driver impairment, driver fatigue, and driving simulation, p 155, 1995 [4]). Selected features were us

关键词： Roads and streets

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11th international conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Saleh, Sami Abdulla Mohsen Suandi, Shahrel Azmin Ibrahim, Haidi Intelligent Biometric Group School of Electrical and Electronic Engineering Universiti Sains Malaysia Pulau Pinang Nibong Tebal14300 Malaysia

ISBN: (纸本)9789811681288

Coherent motions depict the collective movements of individuals in crowd, which widely exist in physical and biological systems. In recent years, similarity-based clustering algorithms especially Coherent Filtering (CF) clustering approach has gained high popularity in the field of coherent motions detection. CF finds motion clusters of different scale, density, and shape, in the presence of large amount of noise and outliers. the similarity measure function is utilized as the initial base step for determining the relationships among crowd individuals and thus detecting coherent motion from noisy time series data. In this paper, we evaluated the impact of four different similarity measure functions upon CF clustering approach, namely, Chebyshev, Euclidean, Canberra and Cosine, and the results were compared subsequently. Quantitative evaluation and comparison are conducted on synthetic data in two dimensional space. Results reveal that the Euclidean similarity function emerges as the best measure for capturing coherent motions from crowd clutters, while Cosine similarity function performs the worst. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Semi-mind controlled robots based on Reinforcement learning for Indoor Application 8

Semi-mind controlled robots based on Reinforcement Learning ...

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8th international conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Naidji, Ilyes Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Computer Control and Management Engineering Sapienza University of Rome Italy

this paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes. © 2023 Copyright for this paper by its authors.

关键词： Feedback

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Automatic acquisition of immune cells location using deep learning for automated analysis

Automatic acquisition of immune cells location using deep le...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Shoya Kusunose Yuki Shinomiya Takashi Ushiwaka Nagamasa Maeda Yukinobu Hoshino System of Engineering Kochi University of Technology Kochi Japan Information Kochi University of Technology Kochi Japan Kagoshima Uniciersity Kagoshima Japan Kochi Medical School Kochi Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Immune cells are analyzed for the elucidation of the human immune system by researchers. they take a cells' movie and track manually the cells to analyze. But the manual process is hard for them. the aim of this study was reducing the load of theirs with automated cells' picking up. To pick up cells, “Recognition Frequency Space” was generated using immune cells' classifier that was made by CNN that a method of deep learning. the classifier was trained while comparing several conditions to decide the best performance classifier. Recognition Frequency Space was indicated the number of times cells are recognized from the frame image of the movie. then, it was checked if cells were picked up from high recognized points of the space. As the result, 9 immune cells were picked up from the first frame image. the result indicates that multiple immune cells can be picked up automatically if it is used the space, and the method can reduce the load of the cell's analyst.

关键词： Immune system Deep learning Motion pictures Image recognition Training Feature extraction Cells (biology)

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Quantitative Analysis of Influence of Morphological Feature Selection on Neuron Classification

Quantitative Analysis of Influence of Morphological Feature ...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Jianyang Zheng Xianghong Lin College of Computer Science and Engineering Northwest Normal University Lanzhou China

the neuron classification problem is significant for understanding structure-function relationships in computational neuroscience. By solving neuronal classification problem, we can further understand the characteristic of neurons and the process of information transmission. In the training process of the neuron geometry classifier, selecting the appropriate neuron feature data is the premise of obtaining good classification results. First, a data set of 200 three-dimensional neurons was constructed, and there were 10 different types of neurons. Secondly, according to the difference of the extracted neuron characteristic parameters, it is divided into two categories: global features and distribution features. Finally, the machine learning methods such as Logistic Regression, K-Nearest Neighbor, Naive Bayes, Decision Tree, Random Forest, Extremely Randomized Trees and Extreme Gradient Boosting are used to classify neurons, and the effects of different features on neuron classification results are analyzed. the experimental results show: (1) For the K-Nearest Neighbor and Naive Bayes, the distribution feature of the neuron is more critical than the global feature, and even if the effect of the combination of the two will not be improved; (2) the ensemble classifier based on decision trees, such as Random Forests, Extremely Randomized Trees and Extreme Gradient Boosting have better accuracy and stability for neuron classification.

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Telecom Churn Prediction Using Python 11th

Telecom Churn Prediction Using Python

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Sharma, Aditi Tyagi, Mayank Kumar, Sunil Department of Information Technology Meerut Institute of Engineering and Technology Utter Pradesh Meerut India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Utter Pradesh Meerut India

ISBN: (纸本)9789819717231

In this study, a convolutional neural network (CNN) model was put out for the prediction of customer turnover in the telecom sector. In earlier studies, numerous supervised machine learning models were developed and applied to forecast client attrition. However, in order to create these models, attribute selection must be done by humans, which is tedious, time-consuming, tailored to specific datasets, and usually results in attribute selection problems. In order to eliminate human attribute selection and accompanying issues, this study suggested a convolutional neural network model for forecasting consumer churning behaviour. the fourteen thousand data examples obtained from one of the major cellular operators operating in Nigeria were divided into two datasets. Our model was created and put into use using the Python programming language and the Anaconda distribution. Our first preference was Jupyter notebook. A second CNN model, two multi-layer perceptron (MLP) models, and three additional models were created in order to conduct a comparable comparison. the accuracy rates for the MLP models are 81% and 89% for convolution neural network 1 and convolution neural network 2, and 80% and 81% for MLP 1 and MLP 2, respectively. When a company tries to bring in new clients, the costs are far higher than when it tries to lose old ones. All of their plans and strategies are structured to win over existing customers, lose new ones, and ultimately increase customer satisfaction. One of the biggest issues for this company, though, is user churn. It deprives a bank of a variety of income sources, including fees. Furthermore, in the Islamic banking system, the primary source of a bank’s income comes from consumer deposits. It might result in a bank withdrawing some of its deposits. If a bank ignores the loss of these two sources of income and the potential rise in reputational damage, it may be on the verge of failure. the current study offers a preliminary forecast of customer

关键词： Forecasting

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A Transfer learning Based Approach for Skin Lesion Classification from Imbalanced data

A Transfer Learning Based Approach for Skin Lesion Classific...

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international conference on Electrical and Computer engineering (ICECE)

作者： Zillur Rahman Amit Mazumder Ami Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Skin cancer is the most common kind of cancer across the world and some types of skin cancer are deadly if not identified at the early stage. therefore, it is crucial to detect the lesion class as early as possible. But skin lesion classification is a very challenging task and many automated systems have been developed so far based on different deep learning algorithms. In this study, we have used ResNet, Xception, and DenseNet three state-of-the-art deep learning pre-trained models to classify the skin lesions. For the training and evaluation of our models, we used the HAM10000 dataset and obtained balanced accuracy of 78%, 82%, and 82% for the three models respectively. We then combined the three models using the weighted ensemble technique without any further training and got 85.8% balanced accuracy and this improved other evaluation parameters as well by a significant amount.

关键词： Deep learning Training Computational modeling Skin data models Lesions Skin cancer

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A lightweight model based on YOLOv8n in wheat spike detection 11

A lightweight model based on YOLOv8n in wheat spike detectio...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Ban, Xuyang Liu, Pan Xu, Lei Zhao, Jinling Anhui University School of Electronics and Information Engineering Hefei China Anhui University Anhui Engineering Laboratory of Agro-Ecological Big Data Hefei China

ISBN: (纸本)9798350303513

Wheat is a major cereal crop in China, and its yield and quality play an important role in ensuring national food security. Wheat ears are the key to predicting and evaluating wheat yields, so fast and accurate wheat ear detection on mobile devices and wheat yield counting are of great importance for modern, intelligent agricultural mass production. there are many problems with detecting wheat ears: wheat is densely distributed, overlapping and obscuring each other, leading to error and miss detection;wheat is not easy to distinguish from complex weed backgrounds;and the appearance of wheat ears varies depending on the growth period, color and type of wheat they are in. Existing target detection algorithms have problems such as large models, high computing requirements and long computation times, which are not suitable for configuration on portable devices for real-time field calculations. In order to achieve real-time detection counting in the field, it is crucial to ensure the accuracy of model detection while reducing the number of parameters and computation *** this paper, Global Wheat Head Detection (GWHD) is selected as the dataset, and after comparing YOLOv5, YOLOv7, and YOLOv8, YOLOv8, which is superior in comprehensive model size and accuracy, is chosen as the baseline, and the n-model with smaller model depth and convolution channel is selected. Due to the dense and small size of wheat ears in the dataset, the feature information is lost after repeated downsampling, and it is difficult for the P5 layer detection head with lower resolution to detect the information of small targets. therefore, the P5 feature layer is removed, and the P3 and P4 feature layers, which contain more target information with less downsampling, are used for feature extraction, which can focus more on the detection of small targets of wheat ears and also reduce the model size. Replacing the Conv in the network with a lightweight convolutional layer, Depthwise Conv, lightens the

关键词： Convolution

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intelligent Drug Delivery Car System Using STM32 11

Intelligent Drug Delivery Car System Using STM32

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11th international conference of Information and Communication Technology, ICTech 2022

作者： Zhou, Qianyi Hu, Jiaxing Xu, Yunyi School of Information Engineering Wuhan University-of Technology Wuhan China School of Engineering Wuhan University-of Technology Wuhan China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

this system consists of STM32F407 single chip microcomputer, STM32F103 single chip microcomputer, openmv vision module and k210 vision module are composed. Taking stm32f407 single chip microcomputer as the control core, combined with STM32F103 single chip microcomputer, openmv and k210, the device is designed to be used for drug transportation in wards and isolation wards with large workload in the hospital. the device uses openmv to identify the red solid line on the transportation ground of the hospital, through the dynamic priority scheduling algorithm in PD algorithm, the priority of tasks is dynamically allocated according to the resource requirements of tasks, so as to have greater flexibility in resource allocation and scheduling, realize flexible tracking function and dynamic adjustment process, and make the car drive along the red. K210 visual module is used to reflect the appearance difference through the given training photos, which is as same as the identification number as possible, which is convenient to improve the identification rate. After more identification times, the identification number and its position information can be accurately identified, and the identified number and its position information can be transmitted to stm32f407 single chip microcomputer through serial port communication (considering the need to identify four numbers at the same time, two k210 are installed on the front of each vehicle), stm32f407 single chip microcomputer processes the identification information of k210 and tells STM32F103 single chip microcomputer the movement state of the car at this time through serial port communication. Finally, STM32F103 single chip microcomputer controls the car to move accordingly. the data interaction method of the two cars adopts wireless communication. the system has the advantages of short recognition time, digital recognition accuracy of up to 90% and good stability. It can better complete the drug delivery work required by hospi

关键词： Image segmentation

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