Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase Convolutional Neural Network Model

A vast number of image processing and neural network approaches are currently being utilized in the analysis of various medical conditions. Malaria is a disease which can be diagnosed by examining blood smears. But when it is examined manually by the microscopist, the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear and the expertise of microscopist in examining the smears. Among the various machine learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy. We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell images taken from thin blood smear samples into infected and non-infected with the malaria parasite. The proposed OSICNN model consists of four convolutional layers and is showing comparable results when compared with other state of the art models. The accuracy of identifying parasite in RBC has been found to be 98.3% with the proposed model.

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An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Complexity ◽

10.1155/2020/5843932 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Syed Atif Ali Shah ◽

Irfan Uddin ◽

Furqan Aziz ◽

Shafiq Ahmad ◽

Mahmoud Ahmad Al-Khasawneh ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Learning Algorithms ◽

Single Layer ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Proposed Model ◽

The Cost

Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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Deep learning regression model for antimicrobial peptide design

10.1101/692681 ◽

2019 ◽

Cited By ~ 3

Author(s):

Jacob Witten ◽

Zack Witten

Keyword(s):

Neural Network ◽

Antimicrobial Peptides ◽

Network Architecture ◽

State Of The Art ◽

Machine Learning Techniques ◽

Neural Network Architecture ◽

Antibiotic Resistant ◽

E Coli ◽

Naturally Occurring ◽

Learning Techniques

AbstractAntimicrobial peptides (AMPs) are naturally occurring or synthetic peptides that show promise for treating antibiotic-resistant pathogens. Machine learning techniques are increasingly used to identify naturally occurring AMPs, but there is a dearth of purely computational methods to design novel effective AMPs, which would speed AMP development. We collected a large database, Giant Repository of AMP Activities (GRAMPA), containing AMP sequences and associated MICs. We designed a convolutional neural network to perform combined classification and regression on peptide sequences to quantitatively predict AMP activity against Escherichia coli. Our predictions outperformed the state of the art at AMP classification and were also effective at regression, for which there were no publicly available comparisons. We then used our model to design novel AMPs and experimentally demonstrated activity of these AMPs against the pathogens E. coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Data, code, and neural network architecture and parameters are available at https://github.com/zswitten/Antimicrobial-Peptides.

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Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

JUITA Jurnal Informatika ◽

10.30595/juita.v9i2.11686 ◽

2021 ◽

Vol 9 (2) ◽

pp. 211

Author(s):

Faisal Dharma Adhinata ◽

Gita Fadila Fitriana ◽

Aditya Wijayanto ◽

Muhammad Pajar Kharisma Putra

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Disease Classification ◽

Machine Learning Techniques ◽

Early Detection Of Disease ◽

Global Average ◽

Learning Techniques ◽

Corn Plants

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.

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Machine Learning Techniques in Concrete Mix Design

Materials ◽

10.3390/ma12081256 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1256 ◽

Cited By ~ 16

Author(s):

Patryk Ziolkowski ◽

Maciej Niedostatkiewicz

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Compressive Strength ◽

State Of The Art ◽

Mix Design ◽

Machine Learning Techniques ◽

Concrete Mix Design ◽

Learning Techniques ◽

Concrete Mix

Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.

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Convolutional neural networks for improving image quality with noisy PET data

EJNMMI Research ◽

10.1186/s13550-020-00695-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Josh Schaefferkoetter ◽

Jianhua Yan ◽

Claudia Ortega ◽

Andrew Sertic ◽

Eli Lechtman ◽

...

Keyword(s):

Neural Network ◽

Ground Truth ◽

Lesion Detection ◽

Machine Learning Techniques ◽

Noise Levels ◽

Physician Performance ◽

Learning Techniques ◽

Wide Range ◽

Image Ranking

Abstract Goal PET is a relatively noisy process compared to other imaging modalities, and sparsity of acquisition data leads to noise in the images. Recent work has focused on machine learning techniques to improve PET images, and this study investigates a deep learning approach to improve the quality of reconstructed image volumes through denoising by a 3D convolution neural network. Potential improvements were evaluated within a clinical context by physician performance in a reading task. Methods A wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using the full-count reconstructions as the ground truth. The benefits, over conventional Gaussian smoothing, were quantified across all noise levels by observer performance in an image ranking and lesion detection task. Results The CNN-denoised images were generally ranked by the physicians equal to or better than the Gaussian-smoothed images for all count levels, with the largest effects observed in the lowest-count image sets. For the CNN-denoised images, overall lesion contrast recovery was 60% and 90% at the 1 and 20 million count levels, respectively. Notwithstanding the reduced lesion contrast recovery in noisy data, the CNN-denoised images also yielded better lesion detectability in low count levels. For example, at 1 million true counts, the average true positive detection rate was around 40% for the CNN-denoised images and 30% for the smoothed images. Conclusion Significant improvements were found for CNN-denoising for very noisy images, and to some degree for all noise levels. The technique presented here offered however limited benefit for detection performance for images at the count levels routinely encountered in the clinic.

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Disease Identification in Chilli Leaves using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1061.1291s319 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 325-329

Keyword(s):

Neural Network ◽

Machine Learning ◽

Detection System ◽

Network Models ◽

Detection Algorithm ◽

Machine Learning Techniques ◽

Neural Network Models ◽

The Past ◽

Learning Techniques ◽

Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

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p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2019-0043 ◽

2019 ◽

Vol 2019 (3) ◽

pp. 191-209 ◽

Cited By ~ 1

Author(s):

Se Eun Oh ◽

Saikrishna Sunkam ◽

Nicholas Hopper

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Machine Learning Techniques ◽

Feature Engineering ◽

Engineering Process ◽

Learning Techniques ◽

Wide Range

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

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A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i6.286 ◽

2018 ◽

Vol 6 (6) ◽

pp. 300 ◽

Cited By ~ 3

Author(s):

Feidu Akmel ◽

Ermiyas Birihanu ◽

Bahir Siraj

Keyword(s):

Machine Learning ◽

Software Metrics ◽

Quality Standard ◽

Machine Learning Techniques ◽

Software Systems ◽

Health Care Insurance ◽

Software Defect ◽

Learning Techniques ◽

Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽

10.3390/en14164776 ◽

2021 ◽

Vol 14 (16) ◽

pp. 4776

Author(s):

Seyed Mahdi Miraftabzadeh ◽

Michela Longo ◽

Federica Foiadelli ◽

Marco Pasetti ◽

Raul Igual

Keyword(s):

Machine Learning ◽

Power Systems ◽

Smart Grids ◽

State Of The Art ◽

Smart Cities ◽

Power Grids ◽

Machine Learning Techniques ◽

Learning Techniques ◽

New Research ◽

Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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