Content Mining Techniques for Detecting Cyberbullying in Social Media

The use of social media has become an increasingly popular trend, and it is most favorite amongst teenagers. A major problem concerning teens using social media is that they are often unaware of the dangers involved when using these media. Also, teenagers are more inclined to misuse social media because they are often unaware of the privacy rights associated with the use of that particular media, or the rights of the other users. As a result, cyberbullying cases have a steady rise in recent years and have gone undiscovered, or are not discovered until serious harm has been caused to the victims. This study aims to create an effective algorithm that can be used to detect cyberbullying in social media using content mining. Bullies may not use only one social media to victimize other users. Therefore, the proposed algorithm must detect whether or not a user is victimizing someone through one or more social media accounts, then determine which social media accounts are being used to carry out the victimization. To achieve this goal, the algorithm will collect information from content shared by the users in all of their social media accounts, then will determine which content to extract based on a big data technology involving phrases or words that might be used by cyberbullies. Any extracted data will reveal some insight into whether or not cyberbullying is occurring and trigger appropriate approaches to handle it

Complementary and Alternative Therapy with Traditional Chinese Medicine for Infertility

Infertility results in a country with a low birth rate and an aging population, and thus there is vested interest in treating this problem by using both complementary and alternative therapies, in addition to conventional western medicine. Traditional Chinese medicine (TCM) has been widely used for healthcare in the Eastern world for thousands of years. This chapter describes the evidence to support the role of TCM in the management of male and female infertility

A Survey on Securing Personally Identifiable Information on Smartphones

With an ever-increasing footprint, already topping 3 billion devices, smartphones have become a huge cybersecurity concern. The portability of smartphones makes them convenient for users to access and store personally identifiable information (PII); this also makes them a popular target for hackers. This survey shares practical insights derived from analyzing 16 real-life case studies that exemplify: the vulnerabilities that leave smartphones open to cybersecurity attacks; the mechanisms and attack vectors typically used to steal PII from smartphones; the potential impact of PII breaches upon all parties involved; and recommended defenses to help prevent future PII losses. The contribution of this research is recommending proactive measures to dramatically decrease the frequency of PII loss involving smartphones

Complementary Therapy with Traditional Chinese Medicine for Polycystic Ovarian Syndrome

Polycystic ovarian syndrome (PCOS) is a common, heterogeneous, complex, endocrinopathic condition that causes menstrual dysfunction and infertility in women. Traditional Chinese medicine (TCM) has been widely used for PCOS in Far-East countries for thousands of years. There are significant advantages in treating PCOS with TCM. This chapter aims to investigate the current developments in TCM therapy for PCOS

A Study of Existing Cross-Site Scripting Detection and Prevention Techniques Using XAMPP and VirtualBox

Most operating websites experience a cyber-attack at some point. Cross-site Scripting (XSS) attacks are cited as the top website risk. More than 60 percent of web applications are vulnerable to them, and they ultimately are responsible for over 30 percent of all web application attacks. XSS attacks are complicated, and they often are used in conjunction with social engineering techniques to cause even more damage. Although prevention techniques exist, hackers still find points of vulnerability to launch their attacks. This project explored what XSS attacks are, examples of popular attacks, and ways to detect and prevent them. Using knowledge gained and lessons-learned from analyzing prior XSS incidents, a simulation environment was built using XAMPP and VirtualBox. Four typical XSS attacks were launched in this virtual environment, and their potential to cause significant damage was measured and compared using the Common Vulnerability Scoring System (CVSS) Calculator. Recommendations are offered for approaches to impeding XSS attacks including solutions involving sanitizing data, whitelisting data, implementing a content security policy and statistical analysis tools

A Comparative Study on Machine Learning Algorithms for Network Defense

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several selected benchmarks such as time to build models, kappa statistic, root mean squared error, accuracy by attack class, and percentage of correctly classified instances of the classifier algorithms

A Study of the Wound Healing Mechanism of a Traditional Chinese Medicine, Angelica sinensis, Using a Proteomic Approach

Angelica sinensis (AS) is a traditional Chinese herbal medicine that has been formulated clinically to treat various form of skin trauma and to help wound healing. However, the mechanism by which it works remains a mystery. In this study we have established a new platform to evaluate the pharmacological effects of total AS herbal extracts as well as its major active component, ferulic acid (FA), using proteomic and biochemical analysis. Cytotoxic and proliferation-promoting concentrations of AS ethanol extracts (AS extract) and FA were tested, and then the cell extracts were subject to 2D PAGE analysis. We found 51 differentially expressed protein spots, and these were identified by mass spectrometry. Furthermore, biomolecular assays, involving collagen secretion, migration, and ROS measurements, gave results that are consistent with the proteomic analysis. In this work, we have demonstrated a whole range of pharmacological effects associated with Angelica sinensis that might be beneficial when developing a wound healing pharmaceutical formulation for the herbal medicine

Relationship between borderline personality symptoms and Internet addiction: The mediating effects of mental health problems

Aim To examine the relationship between borderline personality symptoms and Internet addiction as well as the mediating role of mental health problems between them. Methods A total of 500 college students from Taiwan were recruited and assessed for symptoms of Internet addiction using the Chen Internet Addiction Scale, borderline personality symptoms using the Taiwanese version of the Borderline Symptom List and mental health problems using four subscales from the Symptom Checklist-90-Revised Scale (interpersonal sensitivity, depression, anxiety, and hostility). Structural equation modeling (SEM) was used to test our hypothesis that borderline personality symptoms are associated with the severity of Internet addiction directly and also through the mediation of mental health problems. Results SEM analysis revealed that all paths in the hypothesized model were significant, indicating that borderline personality symptoms were directly related to the severity of Internet addiction as well as indirectly related to the severity of Internet addiction by increasing the severity of mental health problems. Conclusion Borderline personality symptoms and mental health problems should be taken into consideration when designing intervention programs for Internet addiction