Multilingual Cyberbullying Detection System

Indiana University-Purdue University Indianapolis (IUPUI)Since the use of social media has evolved, the ability of its users to bully others has increased. One of the prevalent forms of bullying is Cyberbullying, which occurs on the social media sites such as Facebook©, WhatsApp©, and Twitter©. The past decade has witnessed a growth in cyberbullying – is a form of bullying that occurs virtually by the use of electronic devices, such as messaging, e-mail, online gaming, social media, or through images or mails sent to a mobile. This bullying is not only limited to English language and occurs in other languages. Hence, it is of the utmost importance to detect cyberbullying in multiple languages. Since current approaches to identify cyberbullying are mostly focused on English language texts, this thesis proposes a new approach (called Multilingual Cyberbullying Detection System) for the detection of cyberbullying in multiple languages (English, Hindi, and Marathi). It uses two techniques, namely, Machine Learning-based and Lexicon-based, to classify the input data as bullying or non-bullying. The aim of this research is to not only detect cyberbullying but also provide a distributed infrastructure to detect bullying. We have developed multiple prototypes (standalone, collaborative, and cloud-based) and carried out experiments with them to detect cyberbullying on different datasets from multiple languages. The outcomes of our experiments show that the machine-learning model outperforms the lexicon-based model in all the languages. In addition, the results of our experiments show that collaboration techniques can help to improve the accuracy of a poor-performing node in the system. Finally, we show that the cloud-based configurations performed better than the local configurations

Study on effect of integrated nutrient management on growth and yield of cauliflower (Brassica oleracea var. botrytis L.)

In order to investigate the effect of integrated nutrient management on growth and yield components in Cauliflower, Brassica oleracea var. botrytis L., cv. Pusa snowball- 16, an experiment was conducted using Randomized Block Design with two replications. The experiment comprised of 14 different treatment combinations comprising of different sources of nutrients including organic, inorganic and bio-fertilizers. The growth parameters like height of plant (12.10, 23.50 cm), number of leaves per plant (9.10, 14.40) and diameter of stem (1.20, 2.10 cm) at 40 and 60 DAT respectively, the days required for curd initiation (62.00 days), days required for curd maturity (85.70 days) and staying period of curd (9.50 days) were observed maximum in treatment combination 75 % RDF + FYM + Azotobacter + Azospirillum (T6). The yield parameters like weight of curd per plot (17.50 kg/plot) and yield per hectare (180.04 q/ha) which was increased 44.10 per cent over RDF. From the studies it can be inferred that the application of 75 % RDF + FYM along with Azospirillum and Azotobactor was found to be the most effective treatment combination for getting enhanced growth and yield in cauliflower

Cyberbullying Detection System with Multiple Server Configurations

Due to the proliferation of online networking, friendships and relationships - social communications have reached a whole new level. As a result of this scenario, there is an increasing evidence that social applications are frequently used for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. To encounter this problem, we have designed a distributed cyberbullying detection system that will detect bullying messages and drop them before they are sent to the intended receiver. A prototype has been created using the principles of NLP, Machine Learning and Distributed Systems. Preliminary studies conducted with it, indicate a strong promise of our approach

Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management using Machine Learning

Communication channel is essential in any type of engagement for delivering and receiving data via the internet. To determine the most efficient and safe way through which network data may travel while minimizing the danger of network breaches or cyber-attacks. The objective is to build an optimized network traffic management predictive model that can predict the ideal path in real-time while accounting through the dynamic nature of software defined network traffic and the continuously changing danger of landscaping. To design a robust model of the data and scalable system that can suggest accurate suggestions of route to the network managers, a thorough grasp of network’s infrastructure, data analysis, and machine learning techniques are applied. Choosing the optimum path route data from the sdn based network traffic dataset, the model suggests an optimal path to avoid network communication traffic and congestion. Here nine Machine Learning algorithms are explored and analysed their performance by using the percentage split, resampling and cross validation which originally recorded as 92.76% and after training with cross validation it improved to 98.40% providing the best optimal path with minimum congestions. Building the optimized network traffic management model not only provide network security but also contribute to environmental sustainability. Their capacity to properly filter and manage network traffic helps to decrease energy usage by predicting the optimal routes for software defined network traffic

Knowledge, Attitude, and Practices of Mothers Related to their Oral Health Status of 6-12 Years Old Children in Bhilai City, Chhattisgarh, India

Aim and Objectives: The aim of the study was to assess the oral health status of 6-12 year old children and their mother’s knowledge, attitude, and practices in Bhilai city. Moreover, this study was also carried out to determine whether mother’s oral health related knowledge, attitude, and practices have a significant influence on the oral health of their children. Materials and Methods: A cross- sectional study was conducted among children (n=600) aged between 6-12 years, attending both government and private schools accompanied with their mothers in Bhilai city. The oral health status of the children was evaluated by using WHO Oral Health Assessment Form (2013). The parents were then asked to fill 25 item based on selfadministered questionnaire. Mother’s knowledge, attitude, and practices were assessed by direct contact with mothers using close ended questionnaire. Statistical Analysis: The data was then entered and analysed using SPSS (Statistical Package for Social Sciences, SPSS Inc., Chicago, IL, USA). Also, p value≤ 0.05 was considered to be statistically significant. Results: The result of the study showed that 90% of mothers had good knowledge, 75% mothers had average attitude, and 51% mothers had high level practices related to oral health. An inverse relationship was found between children’s oral health status and their mother’s knowledge, attitude, and practices about oral health. Thus, the findings were highly significant. Conclusion: Results showed that mother’s oral health related to knowledge, attitude, and practices had a significant impact on oral health status of their children

Stochastic Approach for Energy-Efficient Clustering in WSN

Abstract- Wireless sensor networks are self-organizing networks in which sensor nodes with limited resource are scattered in an area of interest to gather information. WSNs need to have effective node2019;s energy management methods for stable and seamless communication. Power efficient clustering is done in WSN to prolong the life of the network. In WSN, many algorithms are developed to save energy of sensor nodes and to increase the lifetime of the network. This paper provides an energy efficient clustering algorithm inspired by prophet routing protocol to enhance the cluster based operation of the nodes. Adaptive learning is implemented for head selection for efficientcommunication. Simulation results confirm the efficiency of the mechanism

Secured Data Outsourcing in Cloud Computing

Cloud computing is a popular technology in the IT world. After internet, it is the biggest thing for IT world. Cloud computing uses the Internet for performing the task on the computer and it is the next- generation architecture of IT Industry. It is related to different technologies and the convergence of various technologies has emerged to be called as cloud computing. It places the application software and databases to the huge data centers, where the supervision of the data and services may not be fully trusted. This unique attribute poses many new security challenges which have not been well understood. In this paper, we develop system which allows customer to use cloud server with various profits and strong securities. So when customer stores his sensitive data on cloud server he should not worry about securities, we also protect customer’s account from malicious behaviors by verifying the result. This result verification mechanism is highly efficient for both cloud server and cloud customer. Covering security analysis and experiment results shows the immediate practicability of our mechanism design. DOI: 10.17762/ijritcc2321-8169.150314

Multilingual Cyberbullying Detection System

As the use of social media has evolved in recent times, so has the ability to cyberbully victims using it. The last decade has witnessed a surge of cyberbullying-this bullying is not only limited to English but also happens in other languages. A large number of mobile device users are in Asian countries such as India. Such a large audience is a fertile ground for cyberbullies -hence, it is very important to detect cyberbullying in multiple languages. Most of the current approaches to identify cyberbullying are focused on English text, and a very few approaches are venturing into other languages. This paper proposes a Multilingual Cyberbullying Detection System for detection of cyberbullying in two Indian languages- Hindi and Marathi. We have developed a prototype that operates across data sets created for these two languages. Using this prototype, we have carried out experiments to detect cyberbullying in these two languages. The results of our experiments show an accuracy up-to 97% and Fl-score up-to 96% on many datasets for both the languages