L. Ramanathan

Bio: L. Ramanathan is an academic researcher from VIT University. The author has contributed to research in topics: Citation analysis & Euclidean distance. The author has an hindex of 4, co-authored 9 publications receiving 52 citations.

Cluster-based distributed architecture for prediction of student’s performance in higher education

Abstract: Educational data mining (EDM) has emerged as a research area in recent years for researchers all over the world from different and related research areas. The EDM obtained knowledge can be used to offer suggestions to the academic planners in higher education institutes to enhance their decision-making process. Literature has suggested various prediction models for predicting the student’s performance. This work proposes the cluster based distributed architecture for predicting the student’s performance. The proposed cluster-based distributed architecture performs the prediction with clustering through Bayesian fuzzy clustering, feature extraction through Kernel-based principal component analysis, and prediction through the proposed Lion–Wolf based deep belief network (LW-DBN). The proposed architecture uses the LW algorithm to find the optimal weights for the DBN. The experimentation of the proposed work is done by collecting a real-time database and measuring the prediction performance through the mean square error (MSE) and root MSE (RMSE). The proposed LW-DBN model has achieved lower error performance than other models with MSE and RMSE values of 0.222606 and 0.050435, for the database.

Image Encryption Using Modified Elliptic Curve Cryptography and Hill Cipher

Abstract: Currently, the digital communication generates millions of digital data using the digital images. These confidential images must be protected from the intruders over transmission in network channels. To protect the data, in this paper, a combination of Modified Elliptic Curve Cryptography (MECC) and Hill Cipher (HC) (MECCHC) method is proposed. Elliptic curve cryptography (ECC) is an asymmetric key encryption and enhanced further using symmetric encryption of Hill Cipher, allowing simple and fast computations over complex encryption methods of ECC. Adding a layer of Hill Cipher over ECC makes it even more difficult for the intruder attack. Hill Cipher encryption involves multiplication of 4 × 4 key matrix with 4 × 4 chunks of image pixels, where the self-invertible key matrix is derived from the elliptic curve parameters which make decryption process easier and faster without computing the matrix inverse. The MECCHC method is used to encrypt grayscale and color images. The process efficiency of the MECCHC method is evaluated using entropy, peak signal to noise ratio, number of pixels change rate, and unified average changing intensity measures.

Predicting Source and Age of Brain Tumor Using Canny Edge Detection Algorithm and Threshold Technique

Abstract: Objective: We propose an iterative method and associated with thresholding technique for detecting the tumor source and the age of tumor. Methods: The technique is based on Euclidean distance with strong edge and weak edge for identifying the spreading area of disease and also detecting the tumor age. The work involves the use of canny edge detection algorithm and thresholding technique, which exploits the information detection of brain tumor source through Magnetic Resonance Image (MRI). This system helps in the calculation of the age of tumor (approximate) using Euclidean distance. Results: Calculation of the age range between 0 -100 as 0th stage, between 100 - 250 as 1st stage, between 250 - 400 as 2nd stage, 400 – 650 as 3rd stage and also detection of the spread area, helps stopping the tumor from invading the neighbor cells thereby reducing the percentage of invasion of cancerous cells. Conclusion: This method provides the simulation output of proposed algorithm in additional noise resilient and improved in edge and well defined tumor detection than the existing algorithm.

Fake online reviews: Literature review, synthesis, and directions for future research

Abstract: Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.

What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018.

Abstract: The purpose of this paper is to update the review of Bornmann and Daniel (2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive).

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

Abstract: The purpose of this paper is to update the review of Bornmann and Daniel (J Doc 64(1):45–80, 2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive).

Smart healthcare in smart cities: wireless patient monitoring system using IoT

Abstract: Recently, the development of building a smart city has been inclined rapidly with the help of emerging technologies in 5G and internet of things (IoT). In addition with smart city, concepts of smart traffic systems and smart health are also being developed. Combining the above two, smart navigation system for ambulances is being coined. When critical patients are referred by an ambulance, there is a lot of time wastage in facilitation of information and the hospital is completely unaware of the patient’s parameters who is arriving beforehand. Therefore, patient monitor and ambulance tracking system is an efficient system used to carry out a quick thirty-second diagnosis using heartbeat, temperature, breath rate sensors to record vital patient parameters required initially by the doctors to start any treatment and remotely transmit these parameters over wireless medium to the hospital even before the ambulance is deployed. The patient also gets an instant way to request an ambulance on touch of a button without having to call up the hospital and also instantly send a short message service (SMS) to an emergency contact giving both the hospital and contact necessary emergency details a lot earlier. This saves a lot of time, each second of which is important to the patient at life risk. The patient can keep a track of the ambulance’s location, which gives them an idea of its arrival and can also get instant navigation toward the nearest hospital for themselves if need be. The device can be kept with the patient and also installed inside the ambulance. The device enables the use of IoT sensors and android applications for better user interaction and efficient information transmission. The model dictated here is potentially outperformed with easiness and in a better way toward smart healthcare monitoring services.

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Abstract: As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.