Breast cancer is one of the most frequently affecting second types of cancer in men and women worldwide. Of the overall types of cancer, 25% of them are breast cancer in women. Erratic development of breast cells resu...
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Breast cancer is one of the most frequently affecting second types of cancer in men and women worldwide. Of the overall types of cancer, 25% of them are breast cancer in women. Erratic development of breast cells results in breast cancer. The growth of cancer increases the metastasizing of the tissues, spreads fast to the other parts of the body, and results in death. The medical industry requires an efficient algorithm to detect and classify the severity level of breast cancers with the metastasis of the affected tissues. Several earlier research works have focused on constructing a computer algorithm to diagnose breast cancer images to detect and classify cancer. The earlier algorithms involved more sub -functions or procedures in completing individual tasks separately, thus increasing the computational and time complexity. This paper introduces a Deep learning Framework (DLF) to diagnose breast images automatically and speedily with less complexity. The proposed DLF includes a few image processing tasks to improve the quality of the input image and increase classification accuracy. Recently, Convolution Neural Network has been used as an extraordinary class of models for image recognition processes. CNN is one of the deep learning models that can extract the entire set of image features and use them for analysis and classification. Thus, this paper implements a deep CNN for diagnosing and classifying benign and malignant cancers from input datasets with Python coding-the deep form of the CNN obtained by increasing the number of hidden layers and epochs. The experiment proves that CNN is highly reliable compared to the existing algorithm.
The conventional tracking-learning-detection (TLD) algorithm is sensitive to illumination changes, clutter, significant changes of target shape between consecutive frames. In addition, low frame rate scenarios result ...
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The conventional tracking-learning-detection (TLD) algorithm is sensitive to illumination changes, clutter, significant changes of target shape between consecutive frames. In addition, low frame rate scenarios result in drift in object position or even missing the object. To solve these problems and enhance the tracking robustness, in this paper, TLD algorithm was extended in two folds. First, Kalman filter and Mean-shift algorithms were combined for the tracking part and second, co-training semi-supervised learning algorithm was used for the learning part of the conventional TLD structure. The Kalman filter estimates the position of the target in the next frame based on the previous positions of the target. This reduces tracking failure. On the other hands, the Mean-shift tracking algorithm is robust to rotation, partial occlusion and scale changing. In the learning part of TLD structure, two training tracking algorithms with two independent classifiers were run on the current frame simultaneously. Its structure makes data of both pools (color features and target templates) update by the results of other algorithm, in addition to the results of the corresponding algorithm in each of the tracking and detection algorithms. Therefore, classifiers can learn faster changing features of the target during the consecutive frames in online tracking process. Finally, the extended structure can solve the problem of lost object in LFR videos tracking and other similar challenges simultaneously. In terms of overlap ratio metric, comparing with conventional TLD and extended kernelized correlation filters (EKCF) algorithms, the success rate of our algorithm under various scenarios has increased by 161.03% and 255.82% respectively and under other scenarios, in terms of precision metric, it has increased by 18.46% and 1479.47%, respectively. Accordingly, comparative evaluations of the proposed method to other top state-of-the-art tracking algorithms under various scenarios present
Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introd...
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ISBN:
(纸本)9781467365963
Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introduction of learning based detectors using integral channel features. A similar push have not yet been seen for the detection sub-problem of TLR, where detection is dominated by methods based on heuristic models. Evaluation of existing systems is currently limited primarily to small local datasets. In order to provide a common basis for comparing future TLR research an extensive public database is collected based on footage from US roads. The database consists of both test and training data, totaling 46,418 frames and 112,971 annotated traffic lights, captured in continuous sequences under a varying light and weather conditions. The learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection(TLD) and the Centroid Neural Network(CNN...
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ISBN:
(纸本)9781467380133
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection(TLD) and the Centroid Neural Network(CNN). The object is unknown ahead of tracking;the model of the object is composed of objects transformed geometrically immediately after tracking. The TLD framework is useful for long-term object tracking in a video stream because the TLD framework applies a novel learning algorithm called P-N learning. We propose a method that applies the CNN algorithm to the TLD framework. The CNN algorithm is an unsupervised learning algorithm that provides a stable result, regardless of initial values of learning coefficients and neurons. The object tracking algorithm discussed in this paper has a higher accuracy than that of TLD in terms of detection. Additionally, it exhibits better processing performance than that of TLD.
This paper proposes a novel learning algorithm of Binary Feedforward Neural Networks (BFNNs) by combining the self-adaptations of both architecture and *** to the learning algorithm of Extreme learning Machine (ELM), ...
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ISBN:
(纸本)9781467372206
This paper proposes a novel learning algorithm of Binary Feedforward Neural Networks (BFNNs) by combining the self-adaptations of both architecture and *** to the learning algorithm of Extreme learning Machine (ELM), our algorithm only adapts the number of hidden neurons and output weights to effectively train BFNNs with a single hidden layer for classification problems. The algorithm consists of two steps including the expanding and pruning phases. During the expandingphase, the algorithm increases the hidden neurons and also searches the weight of the output neuronusing the Perceptron learning Rule to increase the learning accuracy. In the pruning phase, the least relevant hidden neurons measured by a proposed binary neuron's sensitivity are pruned to reduce the complexity of the modeland increase the generalization ability. Experimental results confirmed the feasibility and effectiveness of the proposed algorithm.
Performance forecasting of aeroengines is crucial for achieving better operational efficiency, ensuring safety, reducing costs, and minimizing environmental impact in the aviation industry. It enables engineers and re...
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Performance forecasting of aeroengines is crucial for achieving better operational efficiency, ensuring safety, reducing costs, and minimizing environmental impact in the aviation industry. It enables engineers and researchers to make informed decisions, leading to advancements in technology and the overall evolution of aviation. This study is focused on the effects of flight conditions on the performance of a turbojet, which in consequence affects the environmental aspect of operation. By investigating the relationships between aeroengine efficiency and performance indicators such as thrust, shaft speed, and exhaust gas temperature (EGT), and flight characteristics expressed in terms of environmental and operational conditions, the study seeks to elucidate these connections. The article's significance lies in its successful application of Long Short-Term Memory (LSTM) networks to predict thrust, shaft speed, and EGT variations in turbojet engines under varying flight conditions. Experimental data from a turbojet test bench is processed with deep learning, specifically LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). The model inputs are free stream air speed, compressor inlet pressure, combustor inlet temperature, combustor inlet pressure, turbine inlet temperature, turbine inlet pressure, nozzle inlet pressure and fuel flow, and the outputs are thrust, shaft speed and EGT. Predicted thrust closely aligns with actual thrust values, though with minor discrepancies. Shaft speed predictions exhibit a similar trend, while EGT predictions showcase a comparable pattern with slight variations. Despite the prediction errors, a thorough evaluation of median values, box plots, and probability density functions confirms that the models effectively capture available information, though discrepancies may arise from measurement inaccuracies and initial engine conditions. These results show that it is possible to accurately predict turbojet p
The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases...
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The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.
Customer profiles have rapidly changed over the past few years, with products being requested with more customization and with lower demand. In addition to the advances in technologies owing to Industry 4.0, manufactu...
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Customer profiles have rapidly changed over the past few years, with products being requested with more customization and with lower demand. In addition to the advances in technologies owing to Industry 4.0, manufacturers explore autonomous and smart factories. This paper proposes a decentralized multi-agent system (MAS), including intelligent agents that can respond to their environment autonomously through learning capabilities, to cope with an online machine shop scheduling problem. In the proposed system, agents participate in auctions to receive jobs to process, learn how to bid for jobs correctly, and decide when to start processing a job. The objective is to minimize the mean weighted tardiness of all jobs. In contrast to the existing literature, the proposed MAS is assessed on its learning capabilities, producing novel insights concerning what is relevant for learning, when re-learning is needed, and system response to dynamic events (such as rush jobs, increase in processing time, and machine unavailability). Computational experiments also reveal the outperformance of the proposed MAS to other multi-agent systems by at least 25% and common dispatching rules in mean weighted tardiness, as well as other performance measures.
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection (TLD) and the Centroid Neural Network (C...
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ISBN:
(纸本)9781467380140
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection (TLD) and the Centroid Neural Network (CNN). The object is unknown ahead of tracking;the model of the object is composed of objects transformed geometrically immediately after tracking. The TLD framework is useful for long-term object tracking in a video stream because the TLD framework applies a novel learning algorithm called P-N learning. We propose a method that applies the CNN algorithm to the TLD framework. The CNN algorithm is an unsupervised learning algorithm that provides a stable result, regardless of initial values of learning coefficients and neurons. The object tracking algorithm discussed in this paper has a higher accuracy than that of TLD in terms of detection. Additionally, it exhibits better processing performance than that of TLD.
The paper presents a comparative analysis of performance of various network equipment providers (NEPs) operating radio access network (RAN) by fault correlation using error code. Effectiveness is measured using superv...
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ISBN:
(数字)9789819713264
ISBN:
(纸本)9789819713257;9789819713264
The paper presents a comparative analysis of performance of various network equipment providers (NEPs) operating radio access network (RAN) by fault correlation using error code. Effectiveness is measured using supervisory learning algorithms applied on network alarm data derived from fault management system (FMS) along with key performance indicators (KPIs) derived from performance management system (PMS). Results are based on the ML reinforcement model algorithm is tested with live data stream for one large CSP in India with accuracy of similar to 90% using vendor [Ericsson/Nokia/Huawei] and technology [2G/3G].
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