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From the Desk of the Vice Chancellor

Greetings!

As the Vice Chancellor of this premier university, I am extremely pleased to share this space to converse with all of you. The University of Madras is a post- sesquicentennial institution which has forged a glorious path for itself and has been the site for significant scientific discoveries as well as the beacon for national and regional societal transformation. It has successfully managed to hold aloft its tradition even while keeping up with the emerging trends. One reason for this is the way in which the university has kept alive its interactions with all the stakeholders.

The COVID 19 pandemic has introduced a ‘new normal’ and its impact is felt in the workings of the university as well. The students have been attending the lectures online. While online learning has the advantage of being anytime and anywhere, it has taken away the peer group interaction and peer learning which are integral aspects of the university experience. We look forward to welcoming you to the campus very soon. Meanwhile, our faculty and administration will continue to bring the best to the virtual classrooms. As students, you need to view the restrictions posed by the pandemic as a temporary deterrent. Your focus should be to gain the competencies to conduct application oriented research in order to evolve as useful citizens of our society.

Our university boasts of faculty with a high degree of knowledge and commitment to offer the best in teaching and research. Given the context of the pandemic, we need to redefine our teaching-learning processes to offer the best pedagogic experience to our students. Similarly, the post-pandemic era demands that we hone the employment potential and entrepreneurial capacity of our learners. This necessitates that we focus on sponsored research in cutting edge areas.

Our administrative staff have been the backbone of the university. While we move towards a transparent and complete e-governance model, we need more support from you. When the teaching-learning process at our university – from admission to certification -- is moving into the digital era, your skills and competencies need to keep pace.

At our university, we are gearing up for the final round of NAAC re-accreditation. This has offered us an opportunity to assess our Strengths, Weaknesses, Opportunities and Challenges. Further, it has made us more determined to reiterate our quality benchmarks in teaching, research and extension activities. We are aware of this responsibility and fully prepared for it because, our university has always encouraged individual thinking within the established frameworks. This is echoed in the words of Tim Burners Lee, who initiated the World Wide Web: “We are forming cells within a global brain and we are excited that we might start to think collectively. What becomes of us still hangs crucially on how we think individually.” Let us unite to make the educational experience at the University of Madras a synergy of the best minds and best thoughts.

Prof.Dr.S.Gowri

Vice-Chancellor
University of Madras

S.SasikalaMCA, MPhil, Ph.D

Professor
Department of Computer Science-IDE
6

Awards

52

Publications

79

Seminars / Conference

1

Projects

4

Ph.D Awarded

4

Ph.D Present

Patents
S.No Title Description Patent Year Authority
1 Ensemble Incremental Deep Multiple Layer Perceptron Model – Sentiment Analysis Application Ensemble incremental deep multiple layer perceptron model – sentiment analysis application Abstract: Purpose – The purpose of this work is to enhance the accuracy of classification of streaming big datasets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets. Design/methodology/approach – It is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APS0), accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k- nearest neighbour (FMCCSC-KNN). Findings – The proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively. Originality/value – A novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms. No. of Pages : 12 No. of Claims : 10 2022 Sasikala.S, Renuka Devi.D
2 A Multi-Label Classification of disaster- related tweets with enhanced work embedding ensemble convolutional neural network model Recently, automating the detection and classification of tweets using machine learning methods has been a tremendous help in crises. Word embedding’s are the most effective word vectors for NLP processing using deep learning classifiers. This research proposes a novel method with the Enhanced Embedding from Language Model (EnELMo) for classifying tweets as different categories with higher classification accuracy and precision for the rapid rescue action in the disaster scenario. The proposed EWECNN method comprises an Enhanced ELMo module to handle Crisis word vectors, a Novel ELMo-CNN Architecture module for feature extraction (ECA), and an effective multi-label classification of text using Crisis Word Vector specific CNN-RNN (CWV-CRNN) stacks. Each of these functional modules is purportedly designed to improve the classification. Among the various approaches discussed, the proposed method outperforms the classification of microblog texts with the accuracy as 93.46 percent and the F1-Score as 92.99 percent for multi-classification of tweets which is higher compared with other methods. The proposed multi-label classification of disaster-related text facilitates faster rescue action in a crisis scenario. 2023 Sasikala.S, Aarthi E
3 ARTIFICIAL INTELLIGENCE APPROACH FOR DIABETIC RETINOPATHY SEVERITY DETECTION ABSTRACT Identifying the severity of diabetic retinopathy (DR) in retinal images, which are captured under diverse imaging conditions, poses significant challenges. Typically, DR is classified into five categories: No DR, mild NPDR (nonproliferative diabetic retinopathy), moderate NPDR, severe NPDR, and PDR (proliferative diabetic retinopathy). Artificial intelligence, particularly deep learning, is increasingly used in medical diagnosis, offering enhanced accuracy in classifying retinal images. In this study, retinal images were sourced from the publicly available Kaggle repository and analyzed using an optimized Convolutional Neural Network (CNN) model enhanced with grid search. The model effectively classifies retinal images into the five DR severity stages, aiding ophthalmologists in accurate diagnosis. Experimental results demonstrated that this novel model outperforms traditional CNN approaches, achieving an accuracy rate of 89%. 2024 Shalini.R, Sasikala.S
// FLASH NEWS //
  • University of Madras attains category - 1 status from UGC |  NIRF Ranking - Ranked 39 in University Category 2024 |  University of Madras has been graded A++ in the NAAC Assessment

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