Trustworthy Adaptive AI for Real-Time Intrusion Detection in Industrial IoT Security Mohammad Al Rawajbeh, Amala Jayanthi Maria Soosai, Lakshmana Kumar Ramasamy, Firoz Khan Iot, 2025 Traditional security methods fail to match the speed of evolving threats because Industrial Internet of Things (IIoT) technologies have become more widely adopted. A lightweight adaptive AI-based intrusion detection system (IDS) for IIoT environments is presented in this paper. The proposed system detects cyber threats in real time through an ensemble of online learning models that also adapt to changing network behavior. The system implements SHAP (SHapley Additive exPlanations) for model prediction explanations to allow human operators to verify and understand alert causes while addressing the essential need for trust and transparency. The system validation was performed using the ToN_IoT and Bot-IoT benchmark datasets. The proposed system detects threats with 96.4% accuracy while producing 2.1% false positives and requiring 35 ms on average for detection on edge devices with limited resources. Security analysts can understand model decisions through SHAP analysis because packet size and protocol type and device activity patterns strongly affect model predictions. The system underwent testing on a Raspberry Pi 5-based IIoT testbed to evaluate its deployability in real-world scenarios through emulation of practical edge environments with constrained computational resources. The research unites real-time adaptability with explainability and low-latency performance in an IDS framework specifically designed for industrial IoT security. The solution provides a scalable method to boost cyber resilience in manufacturing, together with energy and critical infrastructure sectors. By enabling fast, interpretable, and low-latency intrusion detection directly on edge devices, this solution enhances cyber resilience in critical sectors such as manufacturing, energy, and infrastructure, where timely and trustworthy threat responses are essential to maintaining operational continuity and safety.
Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation Amala Jayanthi Maria Soosai Arockiam, Elizabeth Shanthi Irudhayaraj Indonesian Journal of Electrical Engineering and Computer Science, 2023 <span>Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.</span>
Recommender System for Predicting Students' Academic performance in association with Cognitive state and Affective state using Sentiment Analysis and Association Rule Mining on the closed ended questionnaire Amala Jayanthi M, Elizabeth Shanthi I, Lakshmana Kumar Ramasamy 2023 9th International Conference on Information Technology Trends Itt 2023, 2023 The recommender systems (RS) are significant in academics, business, and industry. They are frequently employed in various fields, including shopping, music, movies, travel, dining, and writing. Recently, RS can be used in education to suggest student learning styles. This paper proposes a recommender system for predicting student personality with emotions. One of the common recommender system methodologies, collaborative filtering, generates the best suggestions by finding related individuals or things based on their prior transactions. One of the main issues with the collaborative filtering process is the poor accuracy of ideas. This paper uses association rule mining to recommend student personality with emotion based on closed-ended questionnaires. This work initially uses the sentiment analysis technique to identify the student's emotions based on the answer provided for a closed-ended questionnaire. Then, polarity-based sentiment analysis is used to classify student emotions. This paper uses the Association rule mining concept to predict student personality with emotion. This is the first study of a recommender system for the student based on closed-ended questionnaires. The real- world closed-ended questionnaire like Emotional intelligence, Eysenck personality, Self-determination scale, Self-efficacy, Rosenberg's self-esteem, Positive and Negative affect schedule, and Oxford Happiness is used to evaluate the performance of the proposed research work.
Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries: A data-driven approach Arunkumar P. M., Lakshmana Kumar Ramasamy, Amala Jayanthi M. International Journal of E Health and Medical Communications, 2022 A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.
Role of educational data mining in student learning processes with sentiment analysis: A survey Amala Jayanthi M., Elizabeth Shanthi I. Research Anthology on Interventions in Student Behavior and Misconduct, 2022 Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics.
Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity Amala Jayanthi M, Elizabeth Shanthi I Mobile Information Systems, 2022 Sentiment analysis is a prominent research topic in natural language processing, with applications in politics, news, education, product review, and other sectors. Especially in the education sector, sentiment analysis can assist educators in finding students’ feelings about a course on time, altering the teaching plan appropriately and timely to improve the quality of education and teaching. For students, the sentiment analysis can identify emotions, academic performance, behaviour, and so on; the primary purpose of this research paper is to analyze students’ emotions, self-esteem, and efficacy based on closed-ended questionnaires. This paper proposes Quest_SA, which uses the sentiment analysis technique to identify students’ emotions based on the answer provided by a closed-ended questionnaire. The polarity value is assigned for each questionnaire scale. The students’ responses are then gathered using a closed-ended questionnaire, and the student’s emotions are classified using a polarity-based method of sentiment analysis. Finally, sentiment scores and emotion variance were used to evaluate the outcomes. According to the sentiment ratings, students have favourable sentiments and emotions such as unhappy, somewhat happy, and happy. The real-world closed-ended questionnaires such as emotional intelligence, Eysenck, personality, self-determination scale, self-efficacy, Rosenberg’s self-esteem, positive and negative affect schedule, and Oxford happiness questionnaires were used to examine the academic performance with the proposed sentiment analysis. This study inferred that the proposed sentiment analysis preprocessing method with polarity scores is as accurate as the standard value calculation.
Trust-aware routing framework for internet of things S. Sankar, Ramasubbareddy Somula, R. Lakshmana Kumar, P. Srinivasan, M. Amala Jayanthi International Journal of Knowledge and Systems Science, 2021 Establishing security in internet of things (IoT) is a critical challenge, as it is connected to the network's extremely resource-constrained devices. The RPL is a standard routing protocol for IoT. It is well-suited for low power and lossy networks (LLN). The RPL provides little security in the IoT network against various attacks. However, one needs to strengthen the security concern in RPL. So, this paper proposes a trust-aware, energy-based reliable routing (TAER-RPL) for IoT to enhance security among network nodes. The TAER-RPL is taken into account the routing metrics, namely trust, ETX, RER to pick the optimal parent for data transmission. The simulation is conducted in COOJA simulator. TAER-RPL's efficiency is compared with SecTrust-RPL and RPL. The TAER-RPL increases the lifespan of the network by 15%.
Role of Educational Data Mining in Student Learning Processes with Sentiment Analysis: A Survey Amala Jayanthi M., Elizabeth Shanthi I. International Journal of Knowledge and Systems Science, 2020 Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics.
Research contemplate on educational data mining M Amala Jayanthi, R Lakshmana Kumar, Abhijith Surendran, K Prathap 2016 IEEE International Conference on Advances in Computer Applications Icaca 2016, 2017
Improvising the web search results using enhanced lingo algorithm in big data analysis for health care Journal of Advanced Research in Dynamical and Control Systems, 2017
