MACHINE LEARNING SOFTWARE PROJECTS

ABSTRACT - We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII MultiPerson benchmark, both in performance and efficiency. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy.Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software Contact:
 +91-9008001602
 080-40969981

ABSTRACT - : In the previous research on air quality prediction, the research on the problem is usually one-sided, and many problems are solved from a single time dimension. In the research of this problem, this paper starts from the time dimension and the space dimension respectively. Considering the temporal continuity and spatial diffusion of air pollutants, the prediction results of the two dimensions are dynamically combined. Comprehensive consideration of various factors to achieve better prediction results. In order to solve the problem that there are few air quality monitoring stations in cities and there is no monitoring data in a large number of areas, an air quality prediction model is proposed. Contact:
 +91-9008001602
 080-40969981

ABSTRACT -Alzheimer's is the main reason for dementia that affects frequently older adults. This disease is costly especially, in terms of treatment. In addition, Alzheimer's is one of the deaths causes in the old-age citizens. Early Alzheimer's detection helps medical staffs in this disease diagnosis, which will certainly decrease the risk of death. This made the early Alzheimer's disease detection a crucial problem in the healthcare industry. The objective of this research study is to introduce a computer-aided diagnosis system for Alzheimer's disease detection using machine learning techniques. We employed data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) brain datasets. Contact:
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 080-40969981

ABSTRACT - Mental health problems, such as depression in children have farreaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate signs to anticipate mental illness as depression in children and adolescents is vital in making an early and accurate diagnosis to avoid severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4–17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Diseases and pests cause huge economic loss to the apple industry every year. The identification of various apple diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This paper is an attempt to provide the timely and accurate detection and identification of apple diseases. In this study, we propose a deep learning based approach for identification and classification of apple diseases. The first part of the study is dataset creation which includes data collection and data labelling. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai Contact:
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 080-40969981

ABSTRACT - A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process reengineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process reengineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. Contact:
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 080-40969981

ABSTRACT - Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other.Contact:
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ABSTRACT - Decision making about discharge destination for critically ill patients is a highly subjective and multidisciplinary process, heavily reliant on the ICU care team, patients and their caregivers’ preferences, resource demand, staffing, and bed capacity. Timely identification of discharge disposition can be useful in care planning, and as a surrogate for functional status outcomes following critical illness. Although prior research has proposed methods to predict discharge destination in a critical care setting, they are limited in scope and in the generalizability of their findings. We proposed and implemented different machine learning architectures to determine the efficacy of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score as well as the patient characteristics that comprise it to predict the discharge destination for critically ill patients within 24 hours of ICU admission. Contact:
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 080-40969981

ABSTRACT - Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classifiedContact:
 +91-9008001602
 080-40969981

ABSTRACT - Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Contact:
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 080-40969981

ABSTRACT - In this paper, a methodology based on machine learning for fault detection in continuous processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee Eastman Process, selected as the use case of this work, where sensors are distributed throughout an industrial plant. A hybrid feature selection approach based on filters and wrappers, called Hybrid Fisher Wrapper method, is proposed to select the most representative sensors to get the highest detection quality for fault identification. The proposed methodology provides a complete design space of solutions differing in the sensing effort, the processing complexity, and the obtained detection quality. It constitutes an alternative to the typical scheme in Industry 4.0, where multiple distributed sensor systems collect and send data to a centralized cloud. Contact:
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 080-40969981

ABSTRACT - Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Contact:
 +91-9008001602
 080-40969981