Volume-07 , Special Issue-09 , Apr - 2019 Go Back

With the technology growing exponentially, there are a lot of researches and inventions taking place in all the fields. New innovations and discoveries are put forth in the form of research papers. There are thousands of research papers today that pertain to different disciplines such as Computer Science, Mathematics, Biology, Chemistry etc. Finding papers pertaining to a specific domain is time consuming and a tedious task. Classification of papers to a specific discipline, subject or a category reduces the task of searching. This task if done manually consumes lot of human effort and time where as if done automatically, saves the time of users by preventing them from going through the entire research paper. The proposed work uses a novel strategy to automatically classify the research papers based by analyzing the structure of abstracts of research papers to assign them to a specific predefined discipline. Machine Learning technique is used to build a learning model to learn the properties or characteristics of documents manually, in some cases semi automatically, so that the more it gets trained the more efficient will be the model to predict or classify the test documents. Support Vector Machine (SVM) algorithm is used to vectorize the training data set and plot them in an n-dimensional space and then to find the hyper plane that will separate the data into a predefined category. The data is then learnt and later used to categorize the data. The performance of SVM is compared with Naïve Bayes and Decision Tree algorithms also. The experimental result proves the outstanding performance of SVM to predict the category of research papers over the other two algorithms mentioned above. The main objective of the proposed work is to develop a system that has the ability to learn from a training set of data, improvise from the experiences without explicitly programming for it and later classify any research paper given to it into a discipline.

Support Vector Machine (SVM), Bag-Of-Words (BOW), Machine Learning (ML)

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[14] X. Fang, "Inference-Based Naive Bayes: Turning Naive Bayes Cost-Sensitive", IEEE Transactions on Knowledge and Data Engineering 25(10):2302-2313 • October 2013.
[15] C. H. Wan, L. H. Lee, R. Rajkumar, and D. Isa, "A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine", International Journal of Innovative Research in Science, Engineering and Technology, Volume 3, Special Issue 3, March 2014.
[16] Maganti Syamala, Dr N J Nalini, Lakshamanaphaneendra, Dr. R Ragupathy, “Comparative Analysis of Document level Text Classification Algorithms using R”, IOP Conf. Series: Materials Science and Engineering 225 (2017) 012076 doi:10.1088/1757-899X/225/1/012076.
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[18] Autade Sushma G., Dr.Gayatri M.Bhandari, “Text Categorization based on SVM and Bayesian Classification Approach Using Class-Specific Features”, International Journal of Advanced Research in Computer Engineering & Technology,Volume 06, Issue 06, June 2017, ISSN: 2278 – 1323.

Perpetua F Noronha, Prathiba R, Gauthami M, "Automatic Classification of Research Papers to a Predefined Category Using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.47-51, 2019.

Many sensors have emerged for different applications nevertheless only rare of the sensor are in use for agriculture field to identify soil type and nutrients specifications this provides a vast space in research. Numerous agricultural research centers are developed and are still on work as an equipped lab for monitoring these data for farmer’s necessity. Getting soil from farmers processing in the lab and resulting in the required data is a common feature but realistic field monitoring sensors are a challenging task. This framework is to develop an easy man - handle sensor for identifying parameters such as: type of the soil, water scarcity, amount of nutrient present in the soil, type of seed for plantation, fertilizer required for the growth of crop, type of diseases that may infect, crop harvesting and cost estimation after cultivation. Classification of these substantial parameters are made using machine learning techniques and to correlate each parameter with its corresponding attributes to provide continuous field monitoring effective precision agriculture is the proposal work. This work focuses on all the parameter fixed together to a sensor listing out the production and cost estimation of any field.

Machine learning, Soil nutrients, Deep learning, Fertilizers

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K.R. Radhakrishnan, T. KohilaKanagalakshmi, Mohit Agarwal, "Precision Agriculture Using Artificial Intelligence & Machine Learning Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.52-55, 2019.

One of the key aspects of building a good machine learning model is Feature engineering. Feature engineering is a process where we create new features from existing raw features. To create new features, we require domain experts who have knowledge of the subject. By using their knowledge they create new features which are helpful for a machine to learn better. The time taken by the domain experts to understand the data and then create new features is time-consuming and expensive. This problem is addressed with a neural network which will not require domain experts to engineer new features. Current paper deals with the case study pertaining to the data of Human Action Recognition. Using the data, the machine predicts the various physical actions and appearances of a person like if the person is sitting, standing, walking, walking up stairs, and walking downstairs or lying. We compare the accuracy of the model using data which was feature engineered by experts and the model which was not feature engineered by the domain experts.

Machine Learning, Feature Engineering, Domain Knowledge, Human Action Recognition, Neural Networks

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Accelerometer-Derived Posture Information,”Sixth International Workshop on Wearable and Implantable Body SensorNetworks, pp.138-143, 2009.
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Rohit Bohra, Pankaj Karki, Kumudavalli M V, "A Comparative Model of Feature Engineering With and Without Domain Knowledge", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.56-59, 2019.

One of the major key roll concepts in today’s technology is internet of things ,IOT which plays a very major role in daily life , with the help of IOT we can make our surroundings in the automated way. IOT is the concept of machine to machine communication , all the devices can be monitored and controlled in an automated way without the human presence, In this paper we discuss about the use of IOT , various applications of IOT , security issues in IOT. This survey will be helpful for the researchers to know about the IOT as well as introducing the new techniques for implementing the IOT in different applications with more advanced way.

IOT,Applications,Attack

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Sangamithra A, Margaret Mary J, "Analysis of IOT in Daily Life", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.60-62, 2019.

Big Data is the buzz word today. Numerous projects centering big data are booming. It hides in itself vital information which if unearthed would provide great insights into various areas. The growing need of this data is complicating security. Handling big data projects is a challenging task. To prevent a potentially disastrous data breach, big data security should be considered seriously. To prevent security issues, some simple steps should be implemented. It can be done with best practices and internal controls, like protecting against NoSQL Injection points. These points provide a way for attackers to access Big Data. This paper discusses the various big data security threats and different innovative Tech solutions to meet many of the security concerns hindering the Big Data persistence, analysis and presentation.

Big Data, Big data security threats, Data Security, SQL injection, NoSQL injection

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Suneetha V, Sunitha. M, Arshiya, "A Novel Approach on Tech Solutions to Mitigate Big Data Security Threats", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.63-68, 2019.

In this digital world, by virtue of highly diversified data generating technologies – huge amount of data is being churned out by organizations like hospices, banks, e-commerce, retail and supply chain, etc.,. Heaps and loads of big data is being generated every minute, by humans and machines. Because of onset of big data the industries have fundamentally changes their way of handling data. The volume and velocity big data generated from the various sources can be managed and analyzed to take appropriate decisions to benefit the organization. One of the most promising fields where big data analytics can be applied is healthcare. Big healthcare data and its analytics has considerable potential to improve quality of patients’ life, gain valuable insights, prevent diseases, make healthcare more affordable. Securing data of patients and ensuring its security is major concern of data analytics. Unless the privacy and security issues of Big Data are addressed in healthcare industry it cannot be too useful. Invasion of patient privacy is a growing concern in big data analytics as emerging threats and vulnerabilities continue to grow. It is necessary to ensure a secure and sound environment for big data for better future in research by repairing the available solutions. In this paper, we present the security and privacy issues in big data applicable to healthcare industry. Also, we discuss the various Anonymization and Encryption techniques to preserve the privacy of the data, comparing their strengths and limitations.

Healthcare; Healthcare privacy; Big data security; Big data Privacy; Data Anonymization, K-anonymity, T-closeness, L-diversity

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Suneetha V, Srivatsala V, Kumara Swamy Y S, "Big Healthcare Data Privacy Preservation –A Technological Perspective", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.69-75, 2019.

Security is being a hot topic in present digital era. The growing usage of technology for communication generates data which is available everywhere but data security is the important issue which draws the attention of all. Cryptography and security is a notion to secure the network and information transmission through wireless network. With ever progression in digital system security has been appeared as a major concern. In this era of virus and hackers of electronic bugs and electronic fraud security is primary. The concept of hardware security has been normally related with the cybersecurity and cryptography. Cyber-attacks are usually more due to lots of users connected to the internet. The basic issues in guarding the safe transmission of data through the web are concern of the security. This paper emphasizes on the key concepts of cryptography and security on critical infrastructure devices to overcome the threats of computer network security.

Cryptography, Cybersecurity, Network Security, Hardware security

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Aruna Devi.T, Tejaswini S Majjigi, Shyam Vaibhav. M S, "Security Enhancement through Cryptography and Hardware Devices", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.76-79, 2019.

Healthcare in today’s world plays most crucial role in one ‘s life. It is imperative that the information related to health care must reach everyone, to make a healthy and a better world. Digital marketing and branding is a way of imparting information about your product or service through digital medium. Traditional marketing has been replaced largely by digital marketing i.e. “marketing online”. This paper discusses how digital marketing is crucial for healthcare in today’s world. The three main pillars of digital marketing are Content, data and technology. The above three aspects are crucial for healthcare as large amount of data belonging to the consumers/patients are put to use effectively to provide a better service to them. Private corporate healthcare industry and Government healthcare system aim to analyze the above data to devise new health care plans to cater to the patients’ needs. This paper discusses the technologies and tools pertaining to the digital marketing techniques implemented by healthcare industry. Digital marketing in healthcare is need of the hour in the world of advancing technology.

Digital marketing, Healthcare Data, Content, Technology, Consumer Patient, Ecosystem

[1] “Optimizing Technical Ecosystem of Digital Marketing “ Smitha Rao (6) V. Srivatsala (6) V. Suneetha (6) DSCASC, Bangalore, India Artificial Intelligence and Evolutionary Computations in Engineering Systems
[2]Proceedings of ICAIECES 2015 Pages pp 691-703 Copyright 2016 DOI 10.1007/978-81-322-2656-7_63 Print ISBN 978-81-322-2654-3 Online ISBN 978-81-322-2656-7 Series Title Advances in Intelligent Systems and Computing Series Volume 394 Series ISSN 2194-5357 Publisher Springer India
[3]“Seven Big Challenges Facing Healthcare Marketers”-Patricio Robles May 25th 2016 Https://Econsultancy.Com/Seven-Big-Challenges-Facing-Healthcare-Marketers/

Veda D V, Sneha R V , Srivatsala V, "Role of Digital Marketing in Healthcare Ecosystem", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.80-83, 2019.

The emerging and most popular processing technique of Natural Language Processing (NLP) deals with computational algorithms to analyze and process human language. It also represents human language. The application of NLP enabled system ranges from search engine to voice assistant, machine translation and dialogue generation. It is concerned with interaction between computers and natural language such as human language. High-revenue non NLP-domain such as finance, government surveillance or marketing is the areas benefited from NLP. The main objective of this paper to review state-of-the art of NLP, its benefits and various processes in NLP enable the systems to deal with real-time data.

Machine Learning, Internet of Things, Artificial Intelligence, Google, Voice Assistant

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Vinothina V, "Natural Language Processing: Comprehensive Review", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.84-86, 2019.

Fog Computing is another worldview that increases the Cloud storage through figuring assets on the edges of a system. It is clearly well described as a cloud-like stage containing comparative information, computation and application administrations, yet is on a very basic level diverse in that it is decentralized. Furthermore, Fog computing are equipped for preparing a lot of information locally, work on-premise and are completely convenient. It can be on mixed equipment’s. These highlights make the Fog stage extremely reasonable for time and area for touchy applications. For instance, Internet of Things (IoT) Devices having lot of information and its required rapidly process for working. This large area of usefulness driven applications increases numerous confidentiality issues with respect to information, virtualization, isolation, organize, malware and observing. This paper reviews existing papers on Fog figuring applications to resolve regular security holes. Comparable advancements like Edge figuring, Cloudlets and Micro-server farms having same incorporated to give a comprehensive audit process. Most of Fog applications are roused by the longing for usefulness and end-user prerequisites, while the confidentiality aspects are frequently disregarded or measured as a bit of hindsight. This paper also determines the effect of those security issues and conceivable solution, giving prospect security-pertinent headings to those responsible for designing, emerging, and preserving Fog systems.

Iot, Cloud, Fog, localized, Edge computing, Decoy, Hashing

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Keerthi T.R., Mohit Agarwal, "Fog Computing and its Security Issues", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.87-90, 2019.