scholarly journals A New Process Model for the Comprehensive Management of Machine Learning Models

2019 ◽  
Author(s):  
Christian Weber ◽  
Pascal Hirmer ◽  
Peter Reimann ◽  
Holger Schwarz
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Learning Models ◽  
Comprehensive Management ◽  
New Process ◽  
Machine Learning Models

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Zainab Jan ◽  
Noor AI Ansari 2nd ◽  
Osama Mousa 3rd ◽  
Ala Ali E.Abd-Alrazaq 5th ◽  
Mowafa Househ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Scoping Review ◽  
Process Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Inclusion Criteria ◽  
Bipolar Patients ◽  
Machine Learning Models

BACKGROUND Bipolar disorder (BD) is the tenth common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. BD patients have 9–17 years lower lifetime as compared to the normal population. It is a predominant mental disorder but misdiagnosed as depressive disorder that leads to difficulties in the treatment of affected patients. 60% of patients with bipolar disorder are looking for the treatment of depression. However, machine learning provides advanced skills and techniques for the better diagnosis of bipolar disorder. OBJECTIVE This review aims to explore the machine learning algorithms for the detection and diagnosis of bipolar disorder and its subtypes. METHODS The study protocol adapts PRISMA extension guidelines. It explores three databases, which were Google scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, two levels of screening were carried out: the title and abstract review and the full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. RESULTS 573 potential articles were retrieved from three databases. After pre-processing and screening, only 33 articles were identified, which met our inclusion criteria. The most commonly used data belonged to the clinical category (n=22, 66.66%). We identified 8 machine learning models used in the selected studies, Support-vector machines (n=9, 27%), Artificial neural network (n=4, 12.12%) , Linear regression (n=3, 0.9%) , Gaussian process model (n=2, 0.6%), Ensemble model (n=2, 0.6%) , Natural language processing (n=1, 0.3%), Probabilistic Methods (n=1, 0.3%), and Logistic regression (n=1, 0.35%). The most common data utilized was magnetic resonance imaging (MRI) for classifying bipolar patients compared to other groups (n=11, 34%) while the least common utilized data was microarray expression dataset and genomic data. The maximum ratio of accuracy was 98% while the minimum accuracy range was 64%. CONCLUSIONS This scoping review provides an overview of recent studies based on machine learning models used to diagnose bipolar disorder patients regardless of their demographics or if they were assessed compared to patients with psychiatric diagnoses. Further research can be conducted for clinical decision support in the health industry. CLINICALTRIAL Null

scholarly journals Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

2020 ◽  
Author(s):  
◽  
Csaba Brunner
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Intrusion Detection ◽  
Process Model ◽  
Detection Performance ◽  
Learning Models ◽  
Detection Techniques ◽  
Network Intrusion ◽  
Hybrid Detection ◽  
Machine Learning Models

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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