Development of Elementary Machine Learning Education Program to Solve Daily Life Problems Using Sound Data

2021 ◽  
Vol 25 (5) ◽  
pp. 705-712
Author(s):  
Woojong Moon ◽  
◽  
Seunghwan Ko ◽  
Junho Lee ◽  
Jonghoon Kim
Keyword(s):  
Machine Learning ◽  
Education Program ◽  
Daily Life ◽  
Life Problems ◽  
Sound Data

scholarly journals Automatic Malicious Code Classification System through Static Analysis Using Machine Learning

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Sungjoong Kim ◽  
Seongkyu Yeom ◽  
Haengrok Oh ◽  
Dongil Shin ◽  
Dongkyoo Shin
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Classification System ◽  
Daily Life ◽  
Malicious Code ◽  
Access To Information ◽  
Analysis Method ◽  
Information And Communication

The development of information and communication technology (ICT) is making daily life more convenient by allowing access to information at anytime and anywhere and by improving the efficiency of organizations. Unfortunately, malicious code is also proliferating and becoming increasingly complex and sophisticated. In fact, even novices can now easily create it using hacking tools, which is causing it to increase and spread exponentially. It has become difficult for humans to respond to such a surge. As a result, many studies have pursued methods to automatically analyze and classify malicious code. There are currently two methods for analyzing it: a dynamic analysis method that executes the program directly and confirms the execution result, and a static analysis method that analyzes the program without executing it. This paper proposes a static analysis automation technique for malicious code that uses machine learning. This classification system was designed by combining a method for classifying malicious code using a portable executable (PE) structure and a method for classifying it using a PE structure. The system has 98.77% accuracy when classifying normal and malicious files. The proposed system can be used to classify various types of malware from PE files to shell code.

scholarly journals Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johannes Allgaier ◽  
Winfried Schlee ◽  
Berthold Langguth ◽  
Thomas Probst ◽  
Rüdiger Pryss
Keyword(s):  
Machine Learning ◽  
Gender Differences ◽  
Daily Life ◽  
Sound Stimulation ◽  
Life Data ◽  
Symptom Profiles ◽  
Symptom Profile ◽  
The Family ◽  
Gender Impacts ◽  
Over Time

AbstractTinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.

scholarly journals Detection of FAKE NEWS on SOCIAL MEDIA using CLASSIFICATION Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3132-3138
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Information Exchange ◽  
Learning Algorithm ◽  
Daily Life ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Fake News ◽  
Other Information

In today’s world social media is one of the most important tool for communication that helps people to interact with each other and share their thoughts, knowledge or any other information. Some of the most popular social media websites are Facebook, Twitter, Whatsapp and Wechat etc. Since, it has a large impact on people’s daily life it can be used a source for any fake or misinformation. So it is important that any information presented on social media should be evaluated for its genuineness and originality in terms of the probability of correctness and reliability to trust the information exchange. In this work we have identified the features that can be helpful in predicting whether a given Tweet is Rumor or Information. Two machine learning algorithm are executed using WEKA tool for the classification that is Decision Tree and Support Vector Machine.

scholarly journals The Use of Demoralization Scale in Italian Kidney Transplant Recipients

2020 ◽  
Vol 9 (7) ◽  
pp. 2119 ◽  
Author(s):  
Yuri Battaglia ◽  
Luigi Zerbinati ◽  
Giulia Piazza ◽  
Elena Martino ◽  
Sara Massarenti ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Daily Life ◽  
Physical Symptoms ◽  
Assessment System ◽  
Psychiatric Diagnoses ◽  
Kidney Transplant Recipients ◽  
Operating Characteristics ◽  
Life Problems ◽  
Meaning And Purpose ◽  
Demoralization Scale

Demoralization is a commonly observed syndrome in medically ill patients. The risk of demoralization may increase in patients after a kidney transplant (KTRs) because of the stressful nature of renal transplantation, psychosocial challenges, and adjustment needs. No study is available on demoralization amongst KTRs. The purpose of our study was to evaluate the validity of the Italian version of the Demoralization Scale (DS-IT) and the prevalence of demoralization in KTRs. Also, we aimed at exploring the association of the DS-IT with International Classification of Diseases (ICD) psychiatric diagnoses, post-traumatic growth (PTG), psychological and physical symptoms, and daily-life problems. A total of 134 KTRs were administered the MINI International Neuropsychiatric Interview 6.0. and the Diagnostic Criteria for Psychosomatic Research–Demoralization (DCPR/D) Interview. The DS-IT, the Edmonton Symptom Assessment System (ESAS), the Canadian Problem Checklist (CPC), were used to measure demoralization, physical and psychological symptoms, and daily-life problems; also, positive psychological experience of kidney transplantation was assessed with the PTG Inventory. Routine biochemistry and sociodemographic data were collected. Exploratory factor analysis demonstrated a four-dimensional factor structure of the DS-IT, explaining 55% of the variance (loss of meaning and purpose, disheartenment, dysphoria, and sense of failure). DS-IT Cronbach alpha coefficients indicated good or acceptable level of internal consistency. The area under the Receiving Operating Characteristics (ROC) curve for DS-IT (against the DCPR/D interview as a gold standard) was 0.92. The DS-IT optimal cut-off points were ≥20 (sensitivity 0.87, specificity 0.82). By examining the level of demoralization, 14.2%, 46.3%, 24.6%, and 14.6% of our sample were classified as having no, low, moderate, and high demoralization, respectively, with differences according to the ICD psychiatric diagnoses (p < 0.001). DS-IT Total and subscales scores were positively correlated with scores of ESAS symptoms and CPC score. A correlation between DS-IT loss of meaning and purpose subscale and PTGI appreciation of life subscale (p < 0.05) was found. This study shows, for the first time, a satisfactory level of reliability of the DS-IT and a high prevalence of severe demoralization in KTRs.

Engineer-in-Residence: Strategy for Increasing Relevance in Transportation Education

2003 ◽  
Vol 1848 (1) ◽  
pp. 45-49 ◽  
Author(s):  
Daniel Pritchard ◽  
Edward A. Beimborn
Keyword(s):  
Education Program ◽  
Civil Engineering ◽  
Real Life ◽  
Applied Science ◽  
University Of Wisconsin ◽  
Life Problems ◽  
College Of Engineering ◽  
The University ◽  
Education Process

Results are reported of the implementation of an engineer-in-residence concept in the Department of Civil Engineering and Mechanics at the University of Wisconsin–Milwaukee College of Engineering and Applied Science. This concept brings an experienced practitioner to campus specifically to mentor students and faculty in the application of engineering and management principles to real-life problems and to provide additional relevancy to the education process. Success of the concept is measured by evaluations completed by students and faculty. On the basis of the findings of these evaluations, the concept is a promising way to provide expanded relevancy to a transportation education program.

Uber and the making of an Algopticon - Insights from the daily life of Montreal drivers

2020 ◽  
Vol 44 (2) ◽  
pp. 241-260
Author(s):  
Rabih Jamil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Life ◽  
The World ◽  
Taxi Industry ◽  
Platform Economy

Using machine learning and artificial intelligence, Uber has been disrupting the world taxi industry. However, the Uber algorithmic apparatus managed to perfectionize the scalable decentralized tracking and surveillance of mobile living bodies. This article examines the Uber surveillance machinery and discusses the determinants of its algorithmically powered ‘all-seeing power’. The latter is being figured as an Algopticon that reinvents Bentham’s panopticon in the era of the platform economy.

Assessment of functional status and daily life problems faced by elderly in a North Indian city

2019 ◽  
Vol 19 (5) ◽  
pp. 419-425 ◽  
Author(s):  
Sukhpal Kaur ◽  
Ashish Bhalla ◽  
Savita Kumari ◽  
Amarjeet Singh
Keyword(s):  
Functional Status ◽  
Daily Life ◽  
Life Problems ◽  
Indian City

scholarly journals Deep Learning Methods for System Identification of UAV

2020 ◽  
Author(s):  
Brilian Putra Amiruddin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
System Identification ◽  
Unmanned Aerial Vehicles ◽  
Control Systems ◽  
Recurrent Neural Networks ◽  
Excellent Performance ◽  
Learning Methods ◽  
Life Problems ◽  
Novel Algorithms

Nowadays, deep learning is the most prominent subjectin the machine learning field. With the bloom of researchers in this field, numerous novel algorithms are used to solve everyday life problems. The control systems field is one of the subjects that get many impacts of machine learning emergence. System identification of Unmanned Aerial Vehicles (UAV) is one of the control systems problems that could be solved by using deep learning methods. In this paper, Recurrent Neural Networks (RNNs) are applied toidentify the system of UAV. Three different models of Deep RNNs have been tried, and the results implied that the RNNs-1 was giving more excellent performance both on the testing MSE and RMSE with the values equal to 0.0006 and 0.0242, successively.

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