scholarly journals A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 748
Author(s):  
Mehedi Masud ◽  
Niloy Sikder ◽  
Abdullah-Al Nahid ◽  
Anupam Kumar Bairagi ◽  
Mohammed A. AlZain
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Diagnostic Methods ◽  
Learning Approach ◽  
Reliable System ◽  
Colon Cancers ◽  
Classification Framework ◽  
Machine Learning Approach ◽  
Histopathological Images ◽  
Cancer Tissues

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

2021 ◽  
Author(s):  
Md Abu Rumman Refat ◽  
Md. Al Amin ◽  
Chetna Kaushal ◽  
Mst Nilufa Yeasmin ◽  
Md Khairul Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Early Stage ◽  
Learning Approach ◽  
Diabetes Prediction

scholarly journals Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1938 ◽  
Author(s):  
Asmau M. Ahmed ◽  
Olga Duran ◽  
Yahya Zweiri ◽  
Mike Smith
Keyword(s):  
Deep Learning ◽  
Contaminated Soils ◽  
Early Stage ◽  
Imaging Techniques ◽  
Rapid Identification ◽  
Hyperspectral Data ◽  
Learning Approach ◽  
Backpropagation Algorithm ◽  
Average Mean Square Error ◽  
Deep Learning Neural Network

Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~ 2 . 2 × 10 - 4 . The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2020 ◽  
Vol 16 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Machine Learning Approach

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

scholarly journals A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2208
Author(s):  
Maria Anna Ferlin ◽  
Michał Grochowski ◽  
Arkadiusz Kwasigroch ◽  
Agnieszka Mikołajczyk ◽  
Edyta Szurowska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Cerebral Microbleeds ◽  
Training Data ◽  
Learning Approach ◽  
Dimensional Problem ◽  
Reliable System ◽  
Machine Learning Approach

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.

Comparative analysis of a deep learning approach with various classification techniques for credit score computation

2020 ◽  
Vol 13 ◽  
Author(s):  
Arvind Pandey ◽  
Shipra Shukla ◽  
Krishna Kumar Mohbey
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Probit Regression ◽  
Credit Score ◽  
Machine Learning Approach

Background: Large financial companies are perpetually creating and updating customer scoring techniques. From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is explored for the case study. Objective: The aim is to audit the comparison between the predictive accuracy of the probability of default with various techniques of statistics and machine learning. Method: In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for machine learning and statistical model evaluation. Results: Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms. Conclusion: Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and statistical approaches.

Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System

2021 ◽  
Vol 16 ◽  
pp. 668-685
Author(s):  
Shankargoud Patil ◽  
Kappargaon S. Prabhushetty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Normal Behavior ◽  
Machine Learning Approach ◽  
Artificial Neural Network Ann ◽  
Static Conditions ◽  
Advanced System

In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.

Bangla Music Genre Classification

2012 ◽  
pp. 124-138
Author(s):  
Ashfaqur Rahman
Keyword(s):  
Machine Learning ◽  
Folk Song ◽  
Learning Approach ◽  
Pop Music ◽  
Classification Framework ◽  
The People ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Ancient Times ◽  
Music Genre Classification

Bangladesh is very rich in its musical history. Music documented the lives of the people from the ancient times. This chapter provides a guideline for classifying Bangla songs into different genres using a machine learning approach. Four different genres, namely Rabindrasangit, Folk song, Adhunik song, and Pop music, were used in the experiments. A set of second order features are used for representing the trend of change of primary features computed over the timeline of the song. The features are incorporated into a number of classification algorithms and a classification framework is developed. The uniqueness of the genres is clearly revealed by high classification accuracies achieved by the different classifiers.

An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

2021 ◽  
pp. 313-337
Author(s):  
P. Poongodi ◽  
E. Udayakumar ◽  
K. Srihari ◽  
Nandan Mohanty Sachi
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Learning Approach ◽  
Machine Learning Approach
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