Qualification: 
MCA, BSc
anir@am.amrita.edu

Ani R. currently serves as Assistant Professor (Sl. Gr.) and Vice Chairperson at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

Ani R., Krishna S., H, A., and U, A., “An Approach Towards Building an IoT Based Smart Classroom”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI) Cite this publication, 2018.[Abstract]


IoT (Internet of Things) is a dynamic innovation with a powerful impact on today's world which can make human life simple and effortless. The scope of this field is limitless and has emerged as a winner in various areas ranging from Medicine, Engineering, Computer Science, Space and Technology, Automobiles and so on. The center of purpose is utilizing IoT based technology in accomplishing automation for classrooms. In this paper, we propose an approach to control and manage electrical equipments such as fans and lights based on human presence. Our focus is towards building a solution which could help in reducing overutilization of energy resources. A camera is used for recognizing the presence of people in the classroom and for analyzing their seating position. Here a classroom is divided into two segments. Whenever a human presence is detected in a particular segment then the light and fan will be switched ON. The reasonable objective of this paper is how to build up a smart classroom where we can automate the electrical equipments with a focus towards energy conservation.

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2018

Conference Paper

Ani R., Jose, J., Wilson, M., and Deepa, O. S., “Modified rotation forest ensemble classifier for medical diagnosis in decision support systems”, in Advances in Intelligent Systems and Computing, 2018, vol. 564, pp. 137-146.[Abstract]


Decision support system (DSS) in medical diagnosis helps medical practitioners in assessing disease risks. The machine learning algorithms prove a better accuracy in predicting and diagnosing diseases. In this study, rotation forest algorithm is being used to analyse the performance of the classifiers in medical diagnosis. The study shows that rotation forest ensemble algorithm with random forest as base classifier outperformed random forest algorithm. In this study, we use linear discriminant analysis (LDA) in place of PCA for feature projection in modified rotation forest ensemble method for classification. The experimental result also reveals that LDA can provide better performance with rotation forest while comparing with PCA. The accuracies given by random forest, rotation forest and proposed modified rotation forest classifiers are 89%, 93% and 95%, respectively. © Springer Nature Singapore Pte Ltd. 2018.

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2017

Conference Paper

Ani R., Maria, E., Joyce, J. J., Sakkaravarthy, V., and Raja, M. A., “Smart Specs: Voice assisted text reading system for visually impaired persons using TTS method”, in 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), Coimbatore, India, 2017.[Abstract]


According to the World Health Organization, out of 7.4 billion population around 285 million people are estimated to be visually impaired worldwide. It is observed that they are still finding it difficult to roll their day today life and it is important to take necessary measure with the emerging technologies to help them to live the current world irrespective of their impairments. In the motive of supporting them We have proposed a smart spec for the blind persons which can perform text detection thereby produce a voice output. This can help the visually impaired persons to read any printed text in vocal form. A specs inbuilt camera is used to capture the text image from the printed text and the captured image is analyzed using Tesseract-Optical Character recognition (OCR). The detected text is then converted into speech using a compact open source software speech synthesizer, eSpeak. Finally, the synthesized speech is produced by the headphone by TTS method. In this project Raspberry Pi is the main target for the implementation, as it provides an interface between camera, sensors, and image processing results, while also performing functions to manipulate peripheral units (Keyboard, USB etc.,).

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2017

Conference Paper

Ani R., Krishna S., Anju, N., Sona, A. M., and Deepa, O. S., “IoT based patient monitoring and diagnostic prediction tool using ensemble classifier”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017-January, pp. 1588-1593.[Abstract]


The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a patient monitoring system for strokeaffected people to minimize future recurrence of the same by alarming the doctor and caretaker on variation in risk factors of stroke disease. Data analytics and decision-making, based on the real-time health parameters of the patient, helps the doctor in systematic diagnosis followed by tailored restorative treatment of the disease. The proposed model uses classification algorithms for the diagnosis and prediction. The ensemble method of treebased classification-Random Forest give an accuracy of 93%. © 2017 IEEE.

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2016

Conference Paper

Dr. Deepa Gopakumar O. S., Ani R., Sasi, G., and Sankar, R., “Decision Support system for diagnosis and prediction of Chronic Renal Failure using Random Subspace Classification”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.[Abstract]


Chronic Renal Failure (CRF) is one of the major disease which affect the human life. The stages of CRF start with loss of renal functions and gradually it leads to complete failure of all kidney functions. This disease is fatal at its end stage unless a replacement of kidney or a dialysis process which is an artificial filtering mechanism is not done. So an early prediction of disease is very important to save the human life. Machine learning is a part of artificial intelligence that uses a variety of techniques to learn from complex dataset. Machine learning techniques are widely used in medical field for disease prediction and prognosis. The objective of this work is to develop a clinical decision support system using machine learning techniques. In this paper first the classification techniques like neural network based back propagation (BPN), probability based Naive Bayes, LDA classifier, lazy learner K Nearest Neighbor (KNN), tree based decision tree, and Random subspace classification algorithms are analyzed. The accuracy of each algorithm found is 81.5%, 78%, 76%, 90%, 93% and 94% respectively on a dataset collected from UCI repository which contains 25 attributes and 400 instances. From the results obtained, the algorithm which gave better result was used for the developing the Clinical Decision Support System.

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2016

Conference Paper

Ani R., Augustine, A., Akhil, N. C., and Dr. Deepa Gopakumar O. S., “Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors”, in Proceedings of the International Conference on Soft Computing Systems, 2016.[Abstract]


Heart diseases are the major cause of death in today’s modern age. Coronary heart disease is one among them. This disease attacks the normal person instantly. Proper diagnosis and timely attention to the patients reduce mortality rate. Proper diagnosis has become a challenging task for the medical practitioners. The cost involved in the immediate treatment or intervention methods are also very expensive. Early diagnosis of the disease using mining of medical data prevents the inattention of occurrence of sudden CHD events. Today, almost all hospitals are using hospital information system and it has huge volume of patient records. This study results in the development of a decision support system using machine intelligence techniques applied on the medical records stored in hospital databases. Classification algorithms are used to evaluate the accuracy of the early prediction of coronary heart events. The classification techniques analyzed are K-nearest neighbor, decision tree-C4.5, Naive Bayes, and the random forest. The accuracy of each technique is found to be 77, 81, 84, and 89 %, respectively. In this study 10-fold cross-validation method is used to measure the unbiased estimate of these prediction models.

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Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

Ani R., Manohar, R., Anil, G., and Dr. Deepa Gopakumar O. S., “Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier”, Biomedical and Pharmacology Journal, vol. 11, pp. 1513-1519, 2018.[Abstract]


In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively

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2016

Journal Article

Dr. Deepa Gopakumar O. S. and Ani R., “Docking studies of Hemochromatosis protein with various compounds of the medicinal plants”, Journal of Chemical and Pharmaceutical Sciences, no. 4, pp. 10-13, 2016.[Abstract]


Hemochromatosis is a genetic disorder which leads to the accretion of iron in parenchymal organs leading to organ toxicity. Normal absorption of iron from daily food is 10% whereas people with hemochromatosis diseases can absorb iron four times more than the normal absorption. There is no proper medication and clinically proved medicines have side effects. Hence an alternative methods is the extraction of bioactive compounds from the medicinal plants to recognize the novel target. 17 compounds from 13 medicinal plants with 81 properties were collected from database. 81 properties with certain specific conditions were checked for accuracy using various machine learning techniques. The compounds that satisfies the pharmacological properties are docked with the mutated protein of hemochromatosis. The free energy and the interaction analysis were also discussed.

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2016

Journal Article

Ani R. and Dr. Deepa Gopakumar O. S., “Rotation Forest Ensemble Algorithm for the Classification of Phytochemicals from the Medicinal Plants”, Journal of Chemical and Pharmaceutical Sciences, no. 4, pp. 14-17, 2016.[Abstract]


Drug Discovery from medicinal plants is an important area in current research and has been providing important source of new drug leads. Plant extracts are proved as main source for many drugs. A major part of traditional therapy uses plant extracts or the associated active principles. Many of the traditional medicines are made as a result of applying some small synthetic modifications of naturally obtained substances. But most of the modern medicines are using synthetic substances instead of natural substances obtained from medicinal plants. Few machine
learning predictive algorithms are applied to classify the compounds to the defined classes and the accuracies of different classification algorithms are analyzed. The present study shows the significance of Rotation Forest ensemble algorithms in the classification of medicinal plant compounds. The other algorithms analyzed are Decision Tree, Random Forest and Naive Bayes. The Random Forest tree based ensemble outperformed the other algorithms in this study.

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2016

Journal Article

Dr. Deepa Gopakumar O. S. and Ani R., “Expectation - Maximization algorithm for protein - Ligand complex of HFE gene”, Journal of Chemical and Pharmaceutical Sciences, vol. 2016, pp. 6-9, 2016.[Abstract]


In the field of pharmaceutical sciences and biomedicine, the issue of protein stabilization presumes meticulous importance. It plays a significant role in purification, formulation, and storage. Suitably folded proteins are usually stable during expression and purification. The interaction between ligands and proteins generally produces changes in protein thermal stability with changes in the midpoint denaturation temperature, enthalpy of unfolding, and heat capacity. The stability of eleven mutations of the proteins corresponding to HFE gene are identified using Random forest and Support vector machine. Various parameters like Half-life period, aliphatic index and GRAVY are computed using online webservers. Based on the machine learning techniques and the computed parameters, the ligands for HFE proteins are obtained. The contact surface area between ligand atom and protein atoms are also identified. The expectation - maximization algorithm was done on the contact surface area to test whether there exist any change in the destabilizing contact between the ligand atom and protein atoms.

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