Communities in Avinashilingam Institute for Home Science and Higher Education for Women - AULIB-IR Central Library
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Energy Efficient Congestion Control Techniques in Wireless Sensor Networks
(Avinashilingam, 2024-06) Vanitha G; Guide - Dr. P.Amudha
In Wireless Sensor Network (WSN), the congestion is controlled by many strategies like congestion detection and avoidance. The rate control is one of the most significant strategies for mitigating the congestion. The priority based rate control algorithms have been proposed in the literature to overcome the congestion due to transmission of the Real Time (RT) data together with the Non-Real Time (NRT) data,but congestion in a network still remains a challenge. The RT data traffic may often be bursty in nature, combined with the high priority NRT data makes the problem more compounded. Neither the fair allocation of bandwidth on different nodes nor prioritizing the traffic class suffices to overcome congestion in a network. As a long queue might sometimes use more than half of the buffer, leading to significant packet loss and delay, a Proficient Rate Control (PRC) algorithm is proposed in the first phase of research by considering Weighted Priority Difference of Differential Rate Control (WPDDRC) with an adaptive priority based system to address the buffer occupancy and queue size. Each child node's input packets are accumulated in one of two virtual queues on a single physical queue, one for low priority traffic and another for high-priority traffic, both are made possible by the PRC algorithm. When a packet is successfully received, the PRC determines whether there is congestion in the virtual
queue and then adjusts the transmission rate of child node accordingly. The PRC technique may control the consequent buffer overflow and congestion in WSN by considering the priority of each traffic type and the current queue status. The Proficient Rate Control with Fair Bandwidth Allocation (PRC-FBA) method is proposed in the second phase of research, with the principles of traffic type priority and equitable assignment of bandwidth. The Signal to Noise Interference Ratio (SINR) model is used for bandwidth distribution in WSN which is used to balance between fairness and performance. Next, a novel utility factor for bandwidth is given in terms of productiveness and fairness. The approximate solution is derived from the sum of the node-to-node computation and the allocation of time slots. Then, the problem has been framed as a non-linear programming problem, partitioned into two halves and the 2-phase approach has been adopted. During the first stage, the connections between nodes are calculated, and in the second stage, time slots are allotted with the goal of optimizing the utility factor. As a consequence, WSNs are able to increase their efficiency and achieve more equitable bandwidth distribution. Proficient Rate Control Data Aggregation Fair Bandwidth Allocation (PRCDA-FBA) is proposed in the third phase of research that makes use of a powerful data aggregation mechanism to maximize the equitable consumption of battery life across all involved nodes. On the other hand, Random Linear Network Coding (RLNC) is used to reduce transmission frequency, increased network channel capacity, which enhanced overall network throughput. The network coding path combines data for transmission to the next hop, increasing channel usage and reducing packet redundancy in the network. When congestion occurs, an adaptive methodology is triggered in which node transmits data using network coding to decrease packet dropping rate. In addition, Long Short-Term Memory (LSTM) recurrent neural networks, which can learn long-term dependencies, enhance the bandwidth allocation of PRCDA-FBA.
They have a temporal dimension that allows them to determine patterns in data sequences. The bandwidth utilized in the past events with parameters like packet drop rate, energy, priority of packets and delay of packets are used to predict the future bandwidth requirements of path.
In the fourth phase of research work, Enhanced Priority Rate Control Data Aggregation Fair Bandwidth Allocation (EPRCDA-FBA) is proposed to further save energy utilization and improve network life time. The major purpose of this work to ensure that Quality of Service (QoS) standards are met in terms of delayed data delivery,reduced energy consumption of energy-intensive nodes and increased network lifespan. This protocol's priority-based technique of regulating data transfer rates considers the node's spare processing capacity of node. Then, a prediction model is utilized to work out how much of a dip in node transmit power can be tolerated without significantly impacting the packet delivery ratio. Then, to avoid overhearing energy-critical nodes, a priority of nodes for delivering traffic classes of packets is determined using a combination of energy.
Securing the VANET through a Hybrid Approach by Mitigating DoS Attacks and its types with Self-healing and Immunization
(Avinashilingam, 2025-01) Rama Mercy S; Guide - Dr. G. Padmavathi
Vehicular Ad Hoc Networks (VANETs), crucial for Intelligent Transportation Systems (ITS), face significant security threats, especially Denial of Service (DoS) and Distributed DoS (DDoS) attacks. These attacks disrupt communication, leading to packet loss, increased latency, and reduced reliability. While existing solutions like trust-based models, cryptographic techniques, and machine learning approaches exist, they often fall
short in detection accuracy, energy efficiency, adaptability to mobile environments, and managing system overhead. This research, titled "Securing VANETs through a Hybrid Approach: Mitigating Denial of Service (DoS) Attacks and its types with Self-healing and Immunization," proposes a three-phase methodology to enhance the security of VANETs. It leverages a hybrid approach having six key contributions with the major objective to secure VANETs—a key part of Intelligent Transportation Systems—from DoS attacks by detecting and preventing these attacks including self-healing and immunization features. The scope of the research tends to focus on DoS attacks and its types with multi-layered defense incorporating security and robustness. In Phase 1, the objective is to detect and isolate vehicles under malicious DoS attacks
with optimized feature selection using GLW-SLFN (Glow-worm Single Layer Feed Forward Neural Network). MCOD-LR (Micro Cluster Outlier Detection and Linear Regression) is applied to detect malicious behavior for multi-class DoS attacks. Furthermore, Kernel Density Estimation and Entropy-based SVM (Support Vector Machine), incorporating trust factors, are leveraged to detect, predict, and classify DoS attacks. A Bayesian aggregate model, in conjunction with Self-healing AIS (Artificial Immune Systems), ensures the continuous monitoring, detection, and isolation of these attacks.The changing topology in VANET remains a challenge in securing VANET operations. This challenge is addressed in phase 2, as the traffic signals are encrypted using Triple Random Hyperbolic Encryption (TRHE) integrated with Hex-Tuple Matched Mapping, which classifies twelve types of DoS attacks. The classification relies on mapping reports and a Deep Trust Factorization Neural Network (DT - NN).Furthermore, to achieve stable data transmission and routing even with dynamic network topology, phase 3 is proposed to immunize the behavior of its clusters by the Deep Trust Factorization Neural Network (which provided trust scores), the Moth Flame Optimization (MFO) Algorithm, and Cache Parallelized Circulation Link Routing (CCL). The system achieved stable data transmission and routing, even with dynamic network topology, due to the immunized behavior of its clusters. The Moth Flame Optimization (MFO) algorithm optimizes the Packet Delivery Rate (PDR) essential for ensuring data is delivered efficiently. This system efficiently creates stable clusters and identifies reliable relay nodes within a VANET. This feature enables the isolation of malicious nodes, directly leading to a significant increase in the Packet Delivery Rate (PDR). The performance of Phase 1 demonstrated significant improvements: a 37% increased detection rate over AODV, 32% over Trust-based methods, and 20% over Firecol. The approach also reduced energy consumption by 38% compared to AODV, Trust-based, and Firecol. Furthermore, it achieved a 25% lower latency, markedly outperforming AODV (95%), Firecol (58%), and the Trust-Based Framework (27%). Building on this, Phases 2 and 3 collectively enhanced overall performance, resulting in a minimized packet loss of 0.5 bits for 200 nodes, a maximized attack detection accuracy
of 97%, and a Packet Delivery Ratio (PDR) of 98%. These figures represent a substantial improvement over existing techniques: Trilateral Trust (42% accuracy, 60% PDR), Host- based Intrusion Detection System (H-IDS) (60% accuracy, 70% PDR), Multi-filter (80% accuracy and PDR), and Stream Position Performance Analysis (SPPA) (90% accuracy and PDR). This approach demonstrates remarkable scalability and adaptability, particularly in challenging environments with high node mobility and dense vehicular traffic. The methods ensure resilient network operations in intelligent transportation systems, delivering reduced energy usage, lower communication delays, and high detection accuracy for secure, reliable, and scalable communication. This research provides highly relevant solutions for real-time VANET applications, effectively incorporating self-healing, immune-inspired mechanisms.
Computation of Nutritional Footprint of Food Consumed by Selected Subjects and Creating Awareness on Planetary Health Diet using the Developed e-Application
(Avinashilingam, 2024-11) N. Komathy; Guide - Dr. R.Radha
The Planetary Health Diet has been developed by the EAT-Lancet Commission; the diet aims to promote both individual well-being and the health of the planet by offering a set of guidelines for sustainable and nutritious food consumption. The study was carried out with the primary objective of promoting a sustainable food ecosystem by following a planetary health diet. The secondary objectives were to analyze the knowledge, attitude, and practice of the planetary health diet among selected subjects, to calculate the carbon and nutritional footprint of the food consumed by selected subjects, to develop and evaluate an e-application for promoting the planetary health diet, and to create awareness about the importance of the planetary health diet and analyze the pre- and post-knowledge levels on the planetary health diet among selected subjects. Nearly 400 women subjects within the age group of 30–50 years were selected using the purposive random sampling method. A survey was also conducted to analyze the pre- and post-knowledge, attitude, and practice of the planetary health diet, and the carbon and nutritional footprint of food consumed by selected subjects after awareness was calculated. Further, an e-application for promoting the planetary health diet was developed. The results highlight that there was a statistically significant negative relationship between the knowledge of the subjects and their adherence to the planetary health diet. This suggests that enhanced knowledge alone may not be sufficient to drive behavior change, and other barriers such as accessibility, cultural preferences, or personal habits may inhibit adherence. The carbon footprint of the study subjects in the 30–35 age groups was higher than all other age groups. Additionally, the 46–50 age groups had the lowest carbon footprint, followed by the 41–45 age groups. Further, there was no significant difference between the implementation of a planetary health diet before and after the awareness program. This implies that while the subjects are cognizant of the significance of a planetary health diet, they fail to put it into practice even after receiving the necessary information and guidance. The planetary health diet is a holistic approach to nutrition that not only promotes human health but also aims to safeguard the well-being of the planet.
Exploring the Factors Influencing Disaster Preparedness Behaviour of Local Residents in Disaster Affected Tourism Destinations in Kerala
(Avinashilingam, 2025-04) Sharon Treesa Abraham; Guide - Dr.V. T. Bindu
Disaster preparedness possesses the capacity to alleviate the detrimental consequences in destinations. Wayanad and Kozhikode districts are well known tourism destinations in Kerala and highly susceptible areas to disasters. The disasters have been continuously affected by disasters in recent years and drastically caused impact on the tourism industry in which the districts rely upon. The local community, tourists, tourism destinations are severely affected by the disasters. Due to the vulnerable situations of the districts, the significance of disaster preparedness cannot be overstated; but preparedness is an ongoing, adaptive process. The study not only addresses residents’ preparedness but how they evolve their behaviours for improving future preparedness efforts. The behaviour centric model developed emphasizes an integrated approach of residents for community-based crisis management. The study was supported by the Theory of Planned Behaviour and Place Attachment Theory to elucidate the preparedness behavior of vulnerable residents in Wayanad and Kozhikode districts. The study addressed the gap on dentification of residents’ behavioural factors and evaluated the causal relationship of influencing factors on Disaster Preparedness Behaviour (DPB); also identified the mediating roles of Self Efficacy (SE), Community Participation Attitude (CPA) and Community Participation Intention (CPI)
between Place Attachment (PA), Risk Perception (RP) and DPB. To investigate the context, questionnaires were distributed to the residents of disaster affected tourism destinations of the districts through stratified sampling. Behavioural Dynamics of Disaster Preparedness
framework was developed to explain the behavioural factors and the SEM model analysis shows overall good fit. The findings of the relationship between the influencing factors and DPB show that significant relationship exists between PA, SE, CPI to DPB. And insignificant
relationship exists between risk perception and DPB. The insignificant relationship shows the compelling necessity for the sensitive destinations through alternative disaster preparedness approach to enhance risk reduction. SE has partial mediation between PA and DPB. CPA and
CPI have mediating roles between RP (Full), PA (Partial) and DPB. The study provides insights to tourism stakeholders in building a framework for disaster preparedness practices to cope with future disasters through psychological and behavioural factors to shape decision- making for related actions. Also contribute to the existing body of literature of behavioural theory of disaster preparedness for sustainable development.
Keywords: Disaster Preparedness Behaviour, Place Attachment, Community Participation Intention, Self Efficacy, Risk Perception
Responsible Tourism Practices and Support for Sustainable Tourism Development: The Perspectives of Local Community in Wayanad
(Avinashilingam, 2025-01) Nimi Markose; Guide - Dr.V .T .Bindu
Sustainable tourism gained significant attention in the 1990s. Responsible Tourism Practices (RTP) emerged as a key framework to ensure tourism development that aims to attain sustainability goals, community benefits, increased environmental protection, and social justice. RTP emphasizes the involvement of local communities, as their engagement and attitudes directly influence tourism's outcomes. Wayanad was selected as a pilot region for RTP implementation in Kerala due to its socio-cultural and environmental significance. However, there needs to be more research on how responsible tourism practices have been applied in Wayanad and their effects on local communities. This study aims to bridge this gap by evaluating the implementation of RTP in Wayanad, focusing on how it affects local communities' perceptions of tourism impacts, their Quality of Life Satisfaction (QOLS), and their support for sustainable tourism development (SSTD). It integrates the Triple Bottom-Line Theory, exploring the interaction between tourism impacts (economic, social, environmental, and cultural), community well-being, RTP, and
OQLS. The research adopts a quantitative methodology, using statistical tools such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modelling (SEM), and Moderation Analysis to test the relationships. The findings reveal a positive relationship between tourism impacts and residents' well-being across various life domains, influencing their overall quality of life satisfaction
and support for sustainable tourism. RTP significantly moderates the effects on economic, social and environmental dimensions of quality of life satisfaction, although it does not significantly moderate the cultural impacts on emotional well-being. These results suggest responsible tourism practices can improve community’s well-being amidst tourism development. Still, there is a need to mitigate the negative effects to enhance emotional well-being. This study contributes to tourism theory by introducing a Responsible Tourism Model that links tourism impacts, Sense of well-being in all life domains, and OQLS to SSTD. The model offers valuable insights for tourism planners and stakeholders, advocating for a more nuanced approach to RTP implementation that addresses both positive and negative tourism impacts. Further research is needed to explore the cultural dimensions of RTP and emotional well-being in depth. Keywords: Tourism Impacts, Sense of well-being in various Life Domains, Overall Quality of Life Satisfaction, Responsible Tourism Practices, Support for Sustainable Tourism Development.
Finger Vein Biometric Authentication using Deep Learning Techniques with Hybrid Labelling and Data Augmentation
(Avinashilingam, 2025-05) Amitha Mathew; Guide - Dr. P. Amudha
Biometric systems, designed to identify individuals through unique biological or behavioural characteristics, have become critical in modern security and identity verification applications. Finger vein authentication, which utilizes the unique vascular patterns within a person’s finger, offers intrinsic security due to the internal and difficult-to-replicate nature of vein patterns. However, the accuracy and reliability of finger vein recognition systems are heavily influenced by the quality of feature extraction techniques and environmental factors, such as motion artifacts, lighting, and finger positioning. This study focuses on developing a motion-tolerant deep learning model for finger vein recognition by employing advanced image labelling and dataset augmentation techniques. Traditional finger vein recognition systems rely on handcrafted features and classical machine learning algorithms, which face limitations in adaptability and accuracy under variable conditions. Recent advancements in deep learning have demonstrated the potential to automatically extract complex patterns from raw image data, addressing these challenges.
In the first phase, a modified UNET and VGG16 model are used for recognition and the performance have been compared on the benchmark datasets, SDUMLA-HMT and THUFV. The U-Net, originally developed for biomedical image segmentation, has been modified by incorporating a fully connected layer, extending its capabilities to include classification tasks. VGG16 outperformed UNET across multiple metrics, with its deep feature extraction capabilities enabling it to capture intricate vein patterns and achieve higher classification accuracy. VGG16 also demonstrated superior precision and recall, minimizing false negatives and improving robustness in practical applications. The second phase explores the impact of using a hybrid labelling algorithm on the VGG16 model’s performance. By accurately labelling finger vein images, the model is able to
learn detailed vein patterns more effectively, leading to improved recognition accuracy and reduced over fitting. This phase emphasized the importance of creating diverse and representative datasets to enhance the model’s generalization ability. In the third phase, various dataset augmentation techniques are investigated, including conventional transformations (rotation, scaling, and flipping) and an advanced approach using Generative Adversarial Networks (GANs). The experimental results revealed that the augmentation techniques significantly enhanced the diversity and quality of the training dataset, further improving the VGG16 model’s recognition performance. In the final phase, an adaptive finger vein recognition system that integrates VGG16 for feature extraction with Long Short-Term Memory (LSTM) for sequence learning has been
developed. This motion-tolerant model is designed to handle real-world conditions, including motion artifacts and environmental variations, ensuring accurate recognition even when the finger is not perfectly still. The architecture was tested on the THUFV and SDUMLA-HMT
datasets, demonstrated improvements in accuracy and robustness. This research demonstrates the effectiveness of combining VGG16 and LSTM architectures along with labelled and augmented dataset, to develop a reliable and scalable motion-tolerant finger vein recognition system. The findings contribute to the advancement of biometric authentication technologies, offering a robust solution for real-world applications.
Prediction of Heart Diseases Risk Using Novel Machine Learning Techniques
(Avinashilingam, 2024-11) Anuradha P; Guide - Dr. Vasantha Kalyani David
Heart diseases are a major health concern globally and early detection of the disease plays a crucial role in reducing mortality rates and improving patient outcomes. With the advancements in machine learning, there is an increasing focus on utilizing these techniques to enhance the prediction and diagnosis of heart diseases, even before symptoms manifest. Machine learning (ML) research in this field aims to develop accurate and efficient models that can assist in early detection, risk assessment, and clinical decision-making. There are a variety of ML methods making use of stand-alone classifiers and hybrids. However, the results from these models vary considerably between various cardiac datasets and/or they are not modeled using both low and high dimensional data. With increased dimensionality of data, it becomes imperative to address shortcomings of existing feature selection and prediction approaches in handling such datasets with complicated feature relationships and
significant degrees of redundancy. This research identifies and addresses issues with existing feature selection and classification methods and proposes novel and improved feature selection and classification techniques towards enhancing and improving heart disease prediction performance. In the first stage of work, feature selection using Feature Importance (FI) ranking of Gradient Boosting algorithms is done and a significant reduction in the search space of feature subsets is identified. Next, this research work proposes a novel feature selection algorithm called ModifiedBoostARoota (MBAR), which identifies the risk parameters that strongly contributes to the prediction of heart disease. This algorithm incorporates CatBoost as the base model and utilizes a novel feature elimination process. In the second phase of the work, a novel Super Learner Ensemble Model (SLEM) is proposed to perform on features selected by MBAR. The SLEM model is an integration of diverse ML base models selected by repeated stratified k-fold cross validation. A meta learner logistic regression is employed to learn from the predictions of the base classifiers. By backward elimination method, an optimal combination of classifiers in SLEM was identified as Catboost and Decision Tree, in order to improve the classification time complexity and performance. The performance of the SLEM model improved when used on features selected by MBAR compared to its performance on datasets with no feature selection. In the third phase of the work, to further improve the classification by selecting an optimal combination of base classifiers in the Super Learner Ensemble Model (SLEM) model, a new Optimized Super Learner Ensemble Model (OSLEM) is proposed. OSLEM utilizes the Whale Optimization Algorithm and pairwise divergence measure to select an optimal base classifier combination. The performance of the final proposed model where ModifiedBoostARoota algorithm is used for feature selection and Optimized Super Learner Ensemble Model (OSLEM) is used for classification was analyzed on both low and high dimensional heart datasets. The proposed model presented high performance in terms of recision, recall, f1-score, specificity and accuracy, when compared with existing models across the heart datasets. The proposed model improved prediction accuracy while being robust against overfitting. Overall, the contributions from this research work would improve the accuracy, efficiency, and decision-making support of heart disease prediction systems, ultimately benefiting clinical practice.
Chitosan nanoencapsulation of garlic and turmeric essential oils for its insecticidal and antifungal activities against stored groundnut
(Avinashilingam, 2024-08) Sindhu M; Guide - Dr. C. A. Annapoorani
Agriculture and food processing are crucial industries in any country, especially in emerging economies. Stored grain insect pests and microbes control mainly depend on synthetic pesticides, recent scientific research is vital in developing new sustainable and eco-friendly approaches. In this study, garlic essential oil (GEO) and turmeric essential oil (TEO) were nanoencapsulated within chitosan nanoparticles (CSNPs) to create an
innovative preservative aimed to protect stored food from insect pests and fungal infection. The GC-MS analysis of GEO identified allyl methyl trisulfide-23.10% and diallyl sulfide-19.47% as the predominant components, while TEO primarily contained α- turmerone-42.0% and β-turmerone-14.0%. The encapsulated of GEO and TEO into chitosan nanoparticles (GEO-CSNPs and TEO-CSNPs) was achieved using the ionic-gelation method and confirmed through TEM micrograph, DLS, XRD, and FT-IR analyses. The in vitro DPPH free radical activity of GEO-CSNPs and TEO-CSNPs showed a significant increase compared to non-encapsulated GEO and TEO. Additionally, GEO-CSNPs and TEO-CSNPs exhibited stronger insecticidal, repellent, and antifeedant activities against adults C. serratus, and T. castaneum, compared to GEO/TEO. Growth regulated gene expression levels of InR and Cyclin E were found to be reduced in the LC20 and LC50 treatment in comparsion to the control group, conversely, LC20 and LC50 treatments exhibited high expression levels of 4EBP and FOXO in T. castaneum. In vitro experiments demonstrated that GEO-CSNPs (1.0 μL/mL) and TEO-CSNPs (0.75μL/mL) effectively inhibited the growth of A. flavus at low doses, while also preventing AFB1 synthesis at concentrations of 0.75 μL/mL and 0.50 μL/mL, compared to the pure GEO and TEO. The biochemical analysis revealed that exposure to GEO-CSNPs and TEO- CSNPs significantly altered the ergosterol level, ions leakage, mitochondrial membrane potential (MMP), and antioxidant system of A. flavus. Similarly, in-situ tests on A .hypogea showed that GEO-CSNPs and TEO-CSNPs at MIC and 2 MIC concentration inhibited fungal growth, AFB1 production, and lipid peroxidation without affecting the seeds. Overall, these experiments suggest that EO-based nanoformulations could enhance insecticidal efficacy against pests, improve antimicrobial activity against stored foodborne pathogens, and serve as innovative preservation agents to extend the shelf life of deposited food products.
Comparative Analysis of Customer Satisfaction towards Reliance Jio and Airtel Selected Mobile Services (with Special Reference to Coimbatore City)
(Avinashilingam, 2023) G.Pavithra; Guide S.Kirthika
A Study on Marketing of Green Cardamom with Reference to Cumbum City (Theni District) – Tamilnadu
(Avinashilingam, 2023) P. Namitha; Guide Dr. K. Abirami