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Item Energy-efficient and load distributed clustering algorithm for dense wireless sensor networks(Modern Education and Computer Science Press, 2017-05-08) Sivaraj, C; Alphonse, P.J.A.; Janakiraman T.N.Wireless sensor networks (WSNs) consist of a large number of tiny sensors with sensing, processing and transmission capabilities. Reducing energy consumption of nodes is one of the major objectives in the design of wireless sensor networks, as sensors have low power batteries. As data collection is the primary objective of WSNs and it consumes more energy, energy-efficient routing is a prominent solution to reduce the sensors energy consumption. This paper proposes an Energy-efficient and Load Distributed Clustering Algorithm (ELDCA) for routing in dense wireless sensor networks to reduce and distribute the sensors' energy consumption. The network is divided into number of virtual congruent square grids of defined sizes. The algorithm constructs optimal and load balanced clusters at every inner crossing point of grid cells using defined cluster fitness value. As early energy depletion is a major design issue in clustering protocols, the proposed algorithm provides a local substitution for energy suffering clusterheads (CHs). To prove the excellence of the proposed algorithm, extensive simulation experiments are performed under different network scenario. The results are compared with latest routing algorithms in terms of network lifetime, energy dissemination, and energy utilization.Item Self-Load balanced clustering algorithm for routing in wireless sensor networks(Modern Education and Computer Science Press, 2017-09-08) Chinnasamy, Sivaraj; Alphonse, P.J.A.; Janakiraman, T.N.Energy-efficient routing is an extremely critical issue in unattended, tiny and battery equipped Wireless Sensor Networks (WSNs). Clustering the network is a promising approach for energy aware routing in WSN, as it has a hierarchical structure. The Connected Dominating Set (CDS) is an appropriate and prominent approach for cluster formation. This paper proposes an Energy-efficient Self-load Balanced Clustering algorithm (SLBC) for routing in WSN. SLBC has two phases: The first phase clusters the network by constructing greedy connected dominating set and the nodes are evenly distributed among them, using the defined parent fitness cost. The second phase performs data manipulations and new on-demand re-clustering. The efficiency of the proposed algorithm is analysed through simulation study. The obtained results show that SLBC outperforms than the recent algorithms like GSTEB and DGA-EBCDS in terms of network lifetime, CDS size, load dissemination, and efficient energy utilization of the network.Item A Hybrid Cryptosystem to Enhance Security in IoT Health Care System(Cambridge University Press, 2019-01-08) Kavitha, S; Alphonse, P.J.A.Internet of Things (IoT) based health care system provides an essential interface between tiny devices and customers, who require customary checking by remedial focus. The technological innovation is important to guarantee the data protection and security among customer and devices, though it is vulnerable by various security assaults. The cryptographic technique is a prominent method for data protection in a healthcare management system. Single cryptographic algorithm based solution suffered to provide efficient security as its high probability of attacks. So this paper proposes a hybrid cryptographic algorithm that secures the sensitive medicinal information in IoT health care system. The proposed hybrid algorithm is dealing with various security attacks in a proficient way, and also increases its performance when compared to other cryptographic algorithms.Item Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation(Springer Nature, 2021-05-01) Alphonse, P.J.A.; Sriharsha, K.V.In recent years, with increase in concern about public safety and security, human movements or action sequences are highly valued when dealing with suspicious and criminal activities. In order to estimate the position and orientation related to human movements, depth information is needed. This is obtained by fusing data obtained from multiple cameras at different viewpoints. In practice, whenever occlusion occurs in a surveillance environment, there may be a pixel-to-pixel correspondence between two images captured from two cameras and, as a result, depth information may not be accurate. Moreover use of more than one camera exclusively adds burden to the surveillance infrastructure. In this study, we present a mathematical model for acquiring object depth information using single camera by capturing the in focused portion of an object from a single image. When camera is in-focus, with the reference to camera lens center, for a fixed focal length for each aperture setting, the object distance is varied. For each aperture reading, for the corresponding distance, the object distance (or depth) is estimated by relating the three parameters namely lens aperture radius, object distance and object size in image plane. The results show that the distance computed from the relationship approximates actual with a standard error estimate of 2.39 to 2.54, when tested on Nikon and Cannon versions with an accuracy of 98.1% at 95% confidence level.Item Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2022-05-25) Chinnasamy, Sivaraj; Naveen, J; Alphonse, P.J.A.; Dhasarathan, Chandramohan; Sambasivam, GThe expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms.Item Using computational fluid dynamics and deep learning for leak detection and localization in a smart water management system(Springer Science and Business Media B.V., 2024-11-05) Pandian, C; Alphonse, P.J.A.Water leakages in distribution systems offer significant challenges, resulting in water loss, infrastructure damage, and environmental hazards. As urban populations grow and water resources become scarcer, water management systems’ efficiency becomes increasingly important. Traditional leak detection methods are inadequate and labour-intensive, making them unsuitable for large-scale infrastructure. In addition, limitations in the data representing pipes of varying diameters and materials under various leak scenarios leads to complexity in automatic water leak detection and localization. In response to this challenge, this research effort combines Computational Fluid Dynamics (CFD) and Deep Learning (DL) to enhance leak detection and localization. The study simulates water flow in pipes of varying diameters and materials under various leak scenarios using CFD. Consequentially deep learning models like the Bi-Layered ReLU Encoder + Softmax and Tri-Layered ReLU Encoder + Softmax are trained and tested. The results indicate improvements in detection accuracy by 2.4% and localization by 5.6%, highlighting the potential of this hybrid approach to enhance the reliability and efficiency of water management systems, ultimately promoting water conservation and reducing infrastructure damage.Item Improving readability through extractive summarization for learners with reading difficulties(Elsevier B.V., 2013-10-19) Nandhini, K; Balasundaram, S.RIn this paper, we describe the design and evaluation of extractive summarization approach to assist the learners with reading difficulties. As existing summarization approaches inherently assign more weights to the important sentences, our approach predicts the summary sentences that are important as well as readable to the target audience with good accuracy. We used supervised machine learning technique for summary extraction of science and social subjects in the educational text. Various independent features from the existing literature for predicting important sentences and proposed learner dependent features for predicting readable sentences are extracted from texts and are used for automatic classification. We performed both extrinsic and intrinsic evaluation on this approach and the intrinsic evaluation is carried out using F-measure and readability analysis. The extrinsic evaluation comprises of learner feedback using likert scale and the effect of assistive summary on improving readability for learners' with reading difficulty using ANOVA. The results show significant improvement in readability for the target audience using assistive summary.Item Challenges in using a standard speech recognition engine in small vocabulary domain(Blue Eyes Intelligence Engineering and Sciences Publication, 2019-07-22) Srinivasan, Narayanan; Balasundaram, S.R.This paper discusses the challenges and proposes recommendations on using a standard speech recognition engine for a small vocabulary Air Traffic Controller Pilot communication domain. With the given challenges in transcribing the Air Traffic Communication due to the inherent radio issues in cockpit and the con-troller room, gathering the corpus for training the speech recognition model is another important problem. Taking advantage of the maturity of today’s speech recognition systems for the standard English words used in the communication, this paper focusses on the challenges in decoding the domain specific named entity words used in the communication.Item Summarizing learning materials using graph based multi-document summarization(IGI Global, 2021-10) Krishnaveni, P; Balasundaram, S.R.The learners and teachers of the teaching-learning process highly depend on online learning systems such as E-learning, which contains huge volumes of electronic contents related to a course. The multi-document summarization (MDS) is useful for summarizing such electronic contents. This article applies the task of MDS in an E-learning context. The objective of this article is threefold: 1) design a generic graph based multi-document summarizer DSGA (Dynamic Summary Generation Algorithm) to produce a variable length (dynamic) summary of academic text based learning materials based on a learner’s request; 2) analyze the summary generation process; 3) perform content-based and task-based evaluations on the generated summary. The experimental results show that the DSGA summarizer performs better than the graph-based summarizers LexRank (LR) and Aggregate Similarity (AS). From the task-based evaluation, it is observed that the generated summary helps the learners to understand and comprehend the materials easily.Item Affix-based distractor generation for tamil multiple choice questions using neural word embedding(Aesthetics Media Services, 2021-05-12) Murugan, Shanthi; Balasundaram, S.R.Assessment plays an important role in learning and Multiple Choice Questions (MCQs) are quite popular in large-scale evaluations. Technology enabled learning necessitates a smart assessment. Therefore, automatic MCQ generation became increasingly popular in the last two decades. Despite a large amount of research effort, system generated MCQs are not useful in real educational applications. This is because of the inability to produce the diverse and human alike distractors. Distractors are the wrong choices given along with the correct answer (key) to confuse the examinee. Especially, in educational domain (grammar learning) the MCQs deal with affix-based or morphologically transformed distractors. In this paper, we present a method for automatic generation of affix-based distractors for fill-in-the-blanks for learning Tamil Vocabulary. Affix-based distractor generation relies on certain regularities manifest in high dimensional spaces. We investigate the quality of distractors generated by a number of criteria, including Part-Of-Speech, difficulty level, spelling, word co-occurrence, semantic similarity and affixation. We evaluated our proposed method in grammar based Multiple Choice Questions (MCQs) dataset. The result shows that affix-based distractors, yield significantly more plausible outcomes in certain grammar based questionsItem Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication(Springer International Publishing, 2022-02-18) Suresh, K; Pal, Rajarshi; Balasundaram, S.R.In an asymmetric-key cryptosystem, the secure storage of private keys is a challenging task. This paper proposes a novel approach for generating the same public and private key pair on a need basis. Hence, the need for secure storage of the private key is done away with. The proposed approach for generating the key pair is based on two factors: fingerprint biometrics and password. A stable binary string is generated from the distances among pairs of minutiae points in a fingerprint using a gray code-based method. Experiments show that gray code representation significantly reduces the number of inconsistencies between the generated bit strings from two instances of the same fingerprint as against the binary code representation. Hence, the Reed–Solomon error correction code successfully corrects errors due to variations in multiple instances of the same fingerprint to induce stability in the generated string. Hash of the stable string generated from the fingerprint and the string generated from hashed password are XORed to derive a stable seed value. The proposed approach uses this seed value to generate two large prime numbers. These prime numbers are used to generate the public and private key pair using the RSA key generation method. This seed value ensures the generation of the same key pair every time. The experimental results show that the proposed approach can ensure a stable generation of the key. It is not required to store either the fingerprint template or the password. Moreover, the generated private key is also not stored. It can be regenerated on a need basis.Item Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques(Sciendo, 2024-06-12) Srinivasan, Narayanan; Balasundaram, S.R.This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms - Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.Item Ensemble learning with RAG model to reduce redundant question topics in auto-generated exam questions(Springer Science and Business Media B.V., 2025-08-01) Sairaj, R. Tharaniya.; Balasundaram, S.R.Reducing redundant question topics during automatic question generation (AQG) is essential for enhancing the quality of test sheets in assessment. Existing AQG models frequently generate repetitive questions due to insufficient named entity (Question Topic) diversity. This study aims to reduce redundancy in auto generated questions by improving diversity in question topics. The methodology is organised in three main phases: first, a fuzzy ontology mapping technique with ensemble learning is applied to generate expanded entity-relationship set generation from external Knowledge Graphs. Second, the generated entity-relationship set is integrated with Retrieval Augmented Generation (RAG) model for source text expansion via augmentation. Third, T5-shd, a pretrained AQG model is adopted to reduce repetition in generated questions. Comparison against baselines such as T5-e2e and T5-ppl shows substantial performance gain as well as reduction in redundancy. Experimental results on various datasets show that the proposed RAG + T5 model reduces redundant question topics along with an improvement in terms of ROUGE-2 metric (up to 5%) and BERTScore (up to 12%) over existing methods. The application of Ensemble Pruning, specifically with the m-EPIC algorithm, further enhances accuracy while reducing computational overhead (around 26%). These findings highlight the efficacy of combining ensemble learning with RAG-based transformers to refine AQG, ensuring improved question diversity and balanced relevance of generated questions. Additionally, this approach helps to reduce model complexity in Automatic Question Generation.Item Enhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models(Dr D. Pylarinos, 2024-11-20) Tella, Ashok Kumar; Balasundaram, S.RThe Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU) can consume clinical resources and be labor intensive. Models that correctly predict LoS are needed to help medical experts make better decisions. To define an ideal process system, healthcare models must consider the patient's condition, availability of beds, resources, etc. These predictions can also help insurance companies manage their budgets. Existing models deploy machines and deep learning techniques to predict LoS. However, there is a need for improvement, considering the features associated with the process. This study presents machine learning algorithms, such as SVM and a stack ensemble, with improved accuracy over existing models. Experiments were carried out on a benchmark dataset, MIMIC-III, specific to ICU patients. The SVM model achieved an accuracy of 93.88%, while the stack ensemble model showed an improved accuracy of 94.70%. The results show that combining machine learning models achieves better prediction rates, which helps healthcare professionals make better decisions.