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Item Energy Efficient Congestion Control Techniques in Wireless Sensor Networks(Avinashilingam, 2024-06) Vanitha G; Guide - Dr. P.AmudhaIn 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.Item 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. PadmavathiVehicular 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.Item 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.RadhaThe 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.Item 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. BinduDisaster 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 PerceptionItem 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 .BinduSustainable 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.Item Finger Vein Biometric Authentication using Deep Learning Techniques with Hybrid Labelling and Data Augmentation(Avinashilingam, 2025-05) Amitha Mathew; Guide - Dr. P. AmudhaBiometric 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.Item Prediction of Heart Diseases Risk Using Novel Machine Learning Techniques(Avinashilingam, 2024-11) Anuradha P; Guide - Dr. Vasantha Kalyani DavidHeart 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.Item 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. AnnapooraniAgriculture 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.Item Impact of Creating Awareness on Environmental Hygienic Practices Among Selected Urban Slum Households(Avinashilingam, 2024-11) Vidhya J; Guide - Dr.K.ManimozhiIndia's urban slums have serious environmental hygiene issues, such as poor waste management, poor sanitation, and a lack of awareness, which pose serious health concerns. The purpose of the study, "Impact of Creating Awareness on Environmental Hygienic Practices Among Selected Urban Slum Households," was to determine the current state of hygiene, gauge the level of awareness among a subset of urban slum women, develop training materials, implement an environmental hygiene awareness campaign, and analyze its results. This study examines the impact of an environmental hygiene awareness program in an urban slum, with a specific focus on how women's knowledge, attitudes, and practices have evolved. 500 households participated in the study, and the results showed that 38 per cent of family heads lacked literacy and 86 per cent of family heads and 67 per cent of homemakers were older than 31. Eighty percent of houses disposed of sewage water incorrectly before the intervention, resulting in poor sanitation and mosquito breeding. Additionally, 87per cent of homes had trash and standing water surrounding them, while only 34per cent of interiors were tidy and clean. Notable gains were noted after the awareness program. The knowledge-practice correlation, which rose from an insignificant level to 0.671, indicated significant behavioral change. Before the intervention, only 20per cent of respondents had strong hygiene awareness, 55 per cent had fair knowledge, and 25per cent had poor understanding. 71 per cent achieved good knowledge after the training, indicating a notable improvement. Similarly, 54 percent of people exhibited good hygiene behaviors, up from 26 percent who practiced poor hygiene and 56 percent who practiced fair hygiene. The frequency study also revealed that 85 per cent of people were aware of the health risks associated with open defecation, and 97 per cent were aware of the need to separate waste into wet and dry categories. Compared to lower levels before the intervention, hand-washing awareness increased, with 31per cent recognizing its significance. Additionally, with the help of NGOs and government organizations, structural improvements, including better drainage and waste management facilities, were implemented. Following awareness training, environmental cleanliness habits showed significant improvement (p < 0.05), as indicated by statistical analysis. The results demonstrate the value of organized awareness and training campaigns in changing personal hygiene practices, thereby improving public health in urban slum areas. To ensure long-term behavioral changes and improvements in environmental cleanliness, this study emphasizes the importance of sustainable intervention strategies and government support. Community involvement, government action, and ongoing education are essential for achieving and maintaining long-lasting hygienic improvements. These findings underscore the importance of continuing support in promoting long-term behavioral change, ultimately leading to a healthier and cleaner environment for residents of urban slums. Key Words: Environmental Hygiene, Households, Hygiene Education, Public Health, Urban Slum, Waste Management.Item Exploring the Essence of Suruti Raga through Musical Forms with Special Reference to the Kritis of Tyagaraja and Muthuswamy Dikshitar(Avinashilingam, 2025-01) Praseeda Bal; Guide - Dr.V.Janaka Maya DeviIn Carnatic music, ragas are having vital role. The concept of raga said to be developed from Matanga’s period (6th- 9th century).It has undergone many changes through the ages but its fundamental characteristics have never been disputed. It is not merely a musical scale, but it is a systematic arrangement of notes. Carnatic music is generally based on two classifications of ragas-Janaka and Janya. There are 72 Janaka ragas (Melakarta ragas) in Carnatic music. Many ragas are derived from these Melakartha ragas are termed as Janya ragas. In Carnatic music Janya ragas are mainly classified into Upanga, Bhashanga,Vakra and Varja. According to the scale of notes these Janya ragas again classified into many groups like Svarantara, Audava, Shadava, Sampurna and many other combinations. The raga Suruti is a gem among the Janya ragas. It is a Janya raga of the 28thMelakartha, Harikambhoji. Suruti is an Upanga Audava Vakra Sampurna Janya raga. Currently it is also considered as an Ubhaya Vakra Shadava Sampurna raga. Raga is the foundation within which all the compositions are being created. Raga is the most significant concept in music composition, and raga classification is pivotal in Indian music theory.Musical forms or compositions have many musical structures, resulting in all shades of a raga. In Carnatic music Suruti raga has utilized almost in all musical forms. This thesis is entitled ‘Exploring the Essence of Suruti Raga through Musical Forms with Special Reference to the Kritis of Tyagaraja and Muthuswamy Dikshitar’ is divided into four chapters excluding introduction and conclusion. The first chapter is tracing out the name of the Parent raga of Suruti: Harikambhoji from selected Lakshanagrandhas. The second chapter is exploring the significance of Suruti raga in various aspects.The third chapter is the execution of svaras of Suruti through various musical compositions of different composers.The fourth chapter,is having the detailed analysis of the Suruti raga compositions of Tyagaraja and Dikshitar. This study is based on the richness of the raga Suruti and tracing out the bhava through different musical compositions of various composers.Item Photodegradation, Anti-bacterial and Self-cleaning Analysis of Surface Modified Zinc Oxide and its Composites for Environmental Remediation(Avinashilingam, 2025-01) Cathelene Antonette L; Guide - Dr. J. ShanthiGlobalization and industrial expansion have increased environmental pollution. Discharge of untreated dye effluents into the waterways disrupts marine ecosystem and also poses considerable risks to human health. Developing nanomaterials as absorbents is an effective alternative for removing the organic and inorganic contaminants, and also an alternative treatment for microbial disinfection. Increased surface area of the nanoparticle facilitates generation of more hydroxyl radicals which participate actively in dye degradation, but they often lead to particle aggregation, which is a significant challenge. Hence, can be reduced using suitable surface modifiers for the photocatalytic nanomaterials. In this work environmentally stable, reusable photocatalytic material was synthesized for reducing environmental degradation. Nanomaterials of ZnO, ZnO/PEG and various combinations of silane (MTMS, MTES, VTMS and VTES) were synthesized by sol-gel technique. The semiconducting metal oxide and its surface modified nanocomposites were tested for their structural, morphological, optical, antibacterial activity, and photocatalytic degradation efficiency with UV and visible light exposure. Photodegradation analysis was done for Methylene blue, Malachite green and textile dye wastewater. ZnO/PEG/VTMS exhibited better degradation efficiency than all other composites due to its reduced crystallite size and PL intensity. The composite exhibited high stability and was found to be reusable even after five cyclic degradation of textile dye wastewater. Hence, this particular sample works as an efficient photocatalyst for dye effluent treatment. All the composites exhibited better antibacterial activity against the bacterial pathogens (Staphylococcus aureus, Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa) than the control (Teicoplanin) used. Enhanced bacterial inhibition to gram positive bacteria than gram negative bacteria were noted. Optically transparent hydrophobic thin films were fabricated using sol-gel based spin coating technique for different silane-PEG combinations. The hydrophobic films with water repellent behavior demonstrated better self-cleaning activity. Stearic acid was deposited on ZnO/PEG/Silane film and its degradation upon UV irradiation was analyzed using FTIR and water contact angle analysis. Upon exposure to UV light, reduction in the hydrophobic behavior of the surface was observed due to photo-induced catalysis, confirming degradation of stearic acid. Among all composite films, ZnO/PEG/VTMS exhibited better degradation of stearic acid. Therefore, the composites aids in reducing environmental degradation.Item Hybrid Transfer Learning Models for Video Anomaly Detection in Surveillance Systems(Avinashilingam, 2025-03) Sreedevi R Krishnan; Guide - Dr. P. AmudhaIn a modern civilized community, public safety is of prime importance, and the detection of anomalous events has become a vital factor for a successful security system. Conventional Video Surveillance (VS) methods are inadequate for identifying anomalous events by themselves. It is due to their inability to analysis large sequential data in dynamic environments. Video Anomaly Detection (VAD) has undergone swift development with the emerging Artificial Intelligence (AI) technologies. The research addresses the challenges of conventional VAD and proposes hybrid Deep learning (DL) models using transfer learning techniques for an efficient VAD system in dynamic environments. The application range of VAD is not limited to but includes social, commercial and industrial surveillance systems. Traffic management in urban areas, crowd management, emergency response and resource optimization are VAD's key application fields. The wide requirement for intelligent VAD inspires the design and development of reliable and efficient video surveillance systems capable of automating Anomaly Detection (AD) with minimal human intervention. The study develops and evaluates four hybrid models for VAD using Deep Learning techniques based on transfer learning to obtain improved performance. The first research phase proposed a CNN-YOLO hybrid model capable of anomaly detection. This model uses CNN for model training and modified YOLOv4 for object detection, ensuring accurate and high-speed anomaly detection. This model processes a single random frame out of 100 input frames and yields a faster response. The CNN-YOLO have high accuracy and faster response but being a small model, it samples a random frame input only. To overcome this limitation and the inability of sequential video processing of the CNN-YOLO model, a hybrid model comprised of Residual Network (ResNet) and Long Short-Term Memory (LSTM) was executed in the second phase. This model can execute feature extraction and sequential information processing in more than thousands of video frames. ResNet-50 is employed for spatial feature extraction and LSTM to capture temporal relationships of the input video data even though this hybrid model enhances detection capability but has low accuracy, efficiency and generalization skill due to overfitting. In the third research phase, a segmentation-based anomaly detection technique is implemented, reducing overfitting. This model is constituted by hybridizing Improved UNet (IUNet) with the Cascade Sliding Window Technique (CSWT). In the IUNet-CSWT hybrid model, standard convolutional layers of IUNet are replaced with a ConvLSTM for spatiotemporal feature extraction. CSWT estimates the anomaly score of the input video and thus classifies it to normal and anomalous events. This model is equipped for processing complex patterns since it has an effective equilibrium between generalization skill and precision. A low false positive rate and high detection accuracy make the model work effectively in crowded environments. The fourth phase implemented a Hierarchical Multiscale-CNN with LSTM model, enhancing multi-scale feature identification and temporal data analysis. This model can work efficiently even in low-resolution video utilizing a Bilateral-Wave Denoise Technique. Multi-scale CNN is augmented by a Spatial Pyramid Pooling (SPP), which enhances the feature extraction. The performance of this model outperforms all the other models.Item Efficacy of Expressive Arts Therapy to enhance Academic Achievement among Learning Disabled Adolescents(Avinashilingam, 2024-10) Vatsala Mirnaalini; Guide - Dr. S. GayatrideviThe current study aimed to enhance academic achievement among Learning Disabled Adolescents using Expressive Arts Therapy and Brain Gym technique. The Research Design used was before, after and follow-up with waitlist control group. Raven’s Standard Progressive Matrices, Schonell: Graded Reading Test, Schonell: Graded Spelling Test, The Schutte Self Report Emotional Intelligence Test, Social Competence Scale, Moss Attention Rating Scale, Youth Disability Screener, Digit Span Test were administered to the participants. Initially 80 participants underwent IQ test and filtered to 70 participants. Out of 70 participants, 66 became part of the study with 35 participants in experimental group and 31 in waitlist control group. The participants in the experimental group took part in the Expressive Arts Therapy and Brain Gym intervention for 8 weeks. Participants in the waitlist control group were administered to the same intervention after follow-up phase. The results revealed that most of the participants had scored moderate Emotional Intelligence, Social Competence, Attention, Working Memory, Academic Achievement and Quality of Life. There was significant difference between Learning Disabled Boy and Girl Adolescent students in Emotional Intelligence. There is a significant difference in Experimental group and Waitlist control group in Emotional Intelligence, Attention and Quality of Life. Expressive Arts Therapy and Brain Gym technique had been effective in enhancing the Emotional Intelligence, Social Competence, Working Memory, Academic Achievement of the Learning Disabled Adolescents in the Experimental Group. From the study, it is imperative that Expressive Arts Therapy can be included in the curriculum to enhance the Academic Achievement of the Learning Disabled students and recommends inclusive education. Key Words: Learning Disability, Expressive Arts Therapy, Emotional Intelligence, Social Competence, Academic AchievementItem Complexity Aware Intelligent Intrusion Detection for Ddos Attacks(Avinashilingam, 2025-01) Kalaivani M; Guide - Dr. G. PadmavathiDistributed Denial of Service (DDoS) attacks pose a major risk to the availability and security of modern network infrastructures. Their growing complexity and scale have outgrown traditional defense methods. Current solutions such as firewalls and standard intrusion detection systems often can't adapt to handle complex and changing intrusion patterns leading to inefficiencies in detection and mitigation. This issue majorly affects industries like finance and e-commerce where security breaches can cause huge damage. To address these problems, this study suggests a new smart Intelligent Intrusion Detection System (IDS) framework that understands complexity. This aims to detect threats with better accuracy, minimized computing power, and have few false alarms. This helps to boost security and availability against the changing world of cyber threats. This thesis aims to create a complexity aware intelligent IDS to fix the problems with current systems. It combines cutting-edge machine learning (ML) and deep learning (DL) models with nature-inspired optimization algorithms to make DDoS attack detection more accurate faster, and stronger. Feature Engineering is the major focus in identifying the right features and making the intrusion detection model better with minimized resources. The novelty of the research lies in developing advanced, complexity-aware intrusion detection systems for DDoS attacks, leveraging innovative methods like Combined Filter for Feature Selection (CFFS), bio-inspired Dragonfly Optimization, Panthera Leo Optimization, and an Attention-Enabled Gated Recurrent Network (AEGRN) to achieve significant detection accuracy, computational efficiency, and adaptability across diverse datasets. A significant contribution of this research is the development of four distinct methodologies. The first contribution enhances the detection of single-vector DDoS attacks using a Combined Filter for Feature Selection (CFFS) integrated with a Decision Tree (DT) classifier. This method achieved an accuracy of 97.69%, with precision and recall exceeding 99% and a false positive rate of 6.32%. However, its performance declined when applied to multiple flooding attacks, indicating the need for more robust techniques. The second contribution introduces the Improved Dragonfly Optimization Algorithm (IDOA) alongside a Decision Tree (DT) classifier to enhance detection accuracy for multi-vector DDoS attacks. This approach achieved 98.89% accuracy, with precision and recall above 97%, an F-score of 98%, demonstrating significant efficiency while leaving room for further improvements in accuracy and efficiency. The third contribution involves an Integrated Intrusion Detection System (IDS) based on the Panthera Leo Optimization (PLO) technique combined with a multilayer feedforward network. This method successfully managed network traffic complexity and variability while maintaining low computational latency. Using the CICDDoS2019 dataset, it achieved a prediction accuracy of 96.8%. The final contribution presents a novel Attention-Enabled Gated Recurrent Network (AEGRN) for detecting DDoS attacks across multiple datasets. This IDS demonstrated over 98% generalization accuracy across various datasets, with an average processing time of 17.4 seconds per epoch. Self-attention maps with BiGRU and feedforward networks proved beneficial in achieving better classification accuracy with reduced complexity and processing time. The proposed models have been evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and computational time. Statistical validation using techniques such as ANOVA and p-tests has confirmed the reliability and significance of the improvements observed. This thesis provides a novel and effective framework for detecting DDoS attacks through the integration of advanced ML, DL, and optimization techniques. The proposed solutions demonstrate notable performance in terms of accuracy, scalability, and computational efficiency, making them suitable for deployment in real-world scenarios. Future research should focus on validating the effectiveness of the developed model on real-time datasets to better reflect real-world cyber threats. Additionally, efforts should be made to assess the model’s capability in identifying and mitigating AI-enhanced and Deep DDoS threats, ensuring robustness against evolving attack strategies that leverage artificial intelligence.Item Effectiveness of Mindfulness Therapy in Managing Performance Anxiety and Enhancing Self efficacy among Hockey Players(Avinashilingam, 2025-01) Anupama N; Guide - Dr. S. GayatrideviHockey is a National Sport of India having rich and long lasting legacy of our country. Performance anxiety not only limits one's capabilities but also prevents one from giving their best effort that is when the athletes experience lower self-efficacy and become mentally weak. A person with high self-efficacy views challenges as things that are supposed to be mastered rather than threats to avoid stated by Albert Bandura (1977). Mindfulness is an essential human capacity to be completely present, conscious and not being affected or overwhelmed by the events occurring around. The study assessed gender variations and relationship between performance anxiety, self-efficacy and mindfulness of hockey players. The study comprises a sample of 49 hockey players (29 male and 20 female) between the ages of 18 – 25 years were selected from the Hockey Stadium at Bangalore. Hockey players were assessed for self-efficacy, performance anxiety and mindfulness using questionnaires. Results proved to have a significant difference between male and female samples on the levels of self-efficacy and performance anxiety characteristics as somatic, worry and concentration disruption. Male hockey players reported with higher levels of performance anxiety compared to females indicating that they were finding difficulty in focusing, and easily distracted from external distractions. Compared to male, female players reported higher levels of self-efficacy. The mindfulness treatment has really facilitated hockey players to manage their performance anxiety and enhance self-efficacy. Behavioural, emotional, cognitive and sleep issues were also significantly improved by enriching their ability to analyse, make goals and deal with emotional problems which in turn reduced their aggressive behaviour, that is especially common in young people. The research study gives a central idea for enlightening the greatness and achievement of hockey players Key Words: Mindfulness Therapy, Self-efficacy, Performance Anxiety and Hockey PlayersItem An in vitro study on the decolourisation of Corafix Yellow GD3R dye using green seaweed Caulerpa taxifolia and its toxicological evaluations(Avinashilingam, 2024-10) Yuvanthi P M; Guide - Dr. M. PoonkothaiCaulerpa taxifolia, a green seaweed was harnessed as the biosorbent for decolourisation of Corafix Yellow GD3R from aqueous solution. The characterization of the biosorbent was performed using point of zero charge and BET were investigated. RSM was employed to determine the optimal variables for dye decolourisation which, includes dye concentration (50-300 mg/L), biosorbent concentration (200-700 mg/L), pH (4-10), and incubation time (24-72hours). The equilibrium studies such as adsorption isotherms, kinetics and thermodynamic were performed to assess the behaviour of the biosorbent.The dealings between the adsorbate and biosorbent were analysed using UV-vis, FTIR spectroscopy, SEM-EDX and XRD. The desorption and regeneration efficiency of the seaweed was estimated under laboratory conditions using various eluents. The physico-chemical parameters of seaweed treated and untreated dye solutions were examined prior to toxicological assessments such as brine shrimp lethality assay, cytogenotoxicity and phytotoxicity. The seaweed was identified as Caulerpa taxifolia and the biosorbent surface was positively charged. The BET analysis of the biosorbent reveals mesoporous structure significantly enhances the surface area available for sorption. RSM assisted BBD revealed that maximum decolorization of 97 % was achieved under optimized conditions, with the dye 100 mg/L and biosorbent concentration of 500 mg/L, pH 8, and at incubation period of 72 hours at room temperature. In equilibrium studies, the adsorption isotherms follow the Langmuir model, directing that adsorption dye onto the biosorbent occurs through chemisorption and characterized as spontaneous, feasible, endothermic process. Analytical studies confirmed the removal of CYGD3R through the accumulation of the dye on the surface of C. taxifolia and shift in the adsorption peaks. The physicochemical parameters of the seaweed treated CYGD3R solution were found to be within permissible limits except TSS, sulphate, phosphate and nitrate. Toxicological assessment confirmed that the seaweed treated dye solution was harmless to both flora and fauna. Also, the biosorbent has decolourisation efficiency on real textile effluents. Hence, the research work focuses on the decolourisation of CYGD3R using C.taxifolia as an biosorbent which is ecofriendly, low cost and not harmful to the ecosystem.Item A Framework for Developing an Enhanced Convolutional Neural Network Based Ensemble Learning Model for Alzheimers Disease Classification Using MRI Brain Images(Avinashilingam, 2025-03) Chithra S; Guide - Dr. R. VijayabhanuThe integration of machine learning techniques in imaging domain is experiencing a deep transformation. It enables systems to analyze massive amount of data, distinguish patterns, and make forecasts with minimal human intervention. Machine Learning is applied to various domains in healthcare sector like disease diagnostics, treatment planning, drug detection and patient management. The machine learning models impact the complex and data exhaustive fields like oncology, cardiology, and neurology. In medical imaging machine learning models can examine MRIs, X-rays to identify irregularities like lumps, fractures, or organ deformities with high accuracy, regularly beating the capabilities of human clinicians. This present study focuses on the brain neuron images in classifying the Alzheimer’s Disease (AD) stages, which aids neurologists to understand complex changes in the brain. Through brain imaging analysis, the study strives to diagnose AD in its premature stages. AD is a deteriorating brain ailment caused by brain cells degeneration that impairs memory and intellectual damage that disturbs lots of old age individuals across the globe. Its a permanent brain ailment that steadily wear away thinking and memory skills which finally disturbs even the basic tasks. The memory and cognitive functions are affected in AD which is the reason for dementia in older population. These computational techniques use algorithms to analyze the brain images to classify patterns and features related to AD. To evaluate medical pictures and to discover neurological ailments like AD computational techniques like Machine Learning (ML) and Deep Learning (DL) techniques are applied in recent times. The objectives are to develop the classification potential of AD stages using ML and DL methods derived from ensemble classification framework. The contributions of this research work primarily focus on the preprocessing framework to eradicate the noises in the brain neuron MRIs by applying various de-noising filters to enhance the image deviations and to attain upgraded classification performance. The segmentation process is performed to for skull removal from the brain neuron image by applying thresholding methods to obtain a perfect image of the brain structure. To address the imbalance problems, a transfer learning approach is used for feature extraction. The first layer is transmitted, followed by the retrieval of features from Convolutional Neural Network using AlexNet model for feature retrieval. Lastly, classification is achieved from the extracted features using ML algorithms such as Decision Tree (DT), K-Nearest neighbor (KNN), Support Vector Machine (SVM), Neuro Evolution of Augmenting Topologies (NEAT), and BAGGING for AD stage classification. This study proposed two hybrid classification techniques like BAGGING_SVM and BAGGING_NEAT. The first hybrid classification technique combines BAGGING and SVM approaches to classify brain neuron images. The second hybrid classification technique combines the BAGGING and NEAT approaches to classify brain neuron images.Item Assessment of Premenstrual Symptoms among College Faculties A Psychosocial Perspective(Avinashilingam, 2024-11) Ramya K; Guide - Dr. K. ManimozhiPremenstrual symptoms are the physical, emotional and behavioural changes that occur in women of reproductive years during the luteal phase of every menstrual cycle.Previous research studies reveal that these symptoms are associated and aggravated with stress, which impacts overall quality of life. Moreover, middle-aged women experience moderate to severe premenstrual mood symptoms slightly more than the other age groups. In general, for the education system to function effectively, professionals in the academic sector have to consistently update and engage in activities to keep up with the current trends for imparting knowledge to the younger generations. Moreover, women faculties face challenges in balancing their dual role as working women as well as homemakers, which leads to stress and other health issues. In this context, reproductive health is quite important for women because it involves monthly hormonal fluctuations, which paves way for emotional disturbances. Since emotional balance is essential for delivering quality education and overall well-being, this study focuses on college faculties emphasizing the psychological aspects of premenstrual symptoms, which are influenced by social factors. The present research attempted to examine the relationships betwe en independent variables such as socio-demographic and health variables and dependent variables including premenstrual, psychosocial, and premenstrual symptom remedial variables. The study had been carried out in five arts and science colleges in the city of Coimbatore, Tamil Nadu, India and the respondents are from both govt-aided and self-financing departments. Statistical analysis was carried out to find the relationships, contributors, and mean differences on various independent and dependent variables. Keeping in view the exploratory nature of the study, thematic analysis was performed to gain insights on recurrent emotional disturbances and to explore possible measures to alleviate symptoms during the luteal phase. The findings revealed that significant relationships exist between independent and dependent variables, which was confirmed with the support of hypotheses. Based on the quantitative and qualitative analysis, a comprehensive framework for work-life balance is proposed along with an assessment measure to alleviate premenstrual symptoms through a biopsychosocial approach. Keywords: Premenstrual, Mood Symptoms, Luteal Phase, Emotional Disturbances, Psychosocial, Work-Life BalanceItem Agricultural Marketing Behaviour and Practices of Rural Farmers in Dibrugarh District, Assam(Avinashilingam, 2025-02) Sushmita Deori; Guide - Dr. S. RajalakshmiAgriculture is a fundamental pillar of Assam's economy, with agricultural marketing playing a crucial role in informing farmers about crop values across various markets. This study, conducted in the Barbaruah Development Block of Dibrugarh district, Assam, aims to analyze the socio-economic characteristics of vegetable farmers, examine their marketing behaviour and practices, assess the knowledge and opinion on agricultural marketing, identify barriers in vegetable marketing and assess the impact of educational awareness on agricultural marketing. A total of 600 vegetable farmers were selected from six villages across two Gram Panchayats using a stratified random sampling method. The study found that sixty-six percent of the farmers were male, while thirty-four percent were female, with forty-three percent classified as marginal farmers. In terms of marketing practices, most of the farmers (93%) harvested their produce early in the morning, sixty-six percent engaged in sorting and grading and forty-two percent washed their produce before sale. Electronic weighing machines were used by fifty-seven percent of the farmers and fifty-three percent traveled 11 to 30 km to reach markets. Weekly markets were the preferred selling point for thirty four percent of the farmers, whereas forty one percent relied on commission agents. For packaging and transportation, seventy-five percent used jute or gunny bags, with bicycles being the most commonly used mode of transport. Also, farmers opted for direct payment and sold their produce based on volume, ensuring efficient market transactions. The findings also revealed that forty-four percent of the farmers exhibited a moderate level of marketing behavior, with a significant relationship observed between e ducational qualifications and marketing behaviour. Among the various influencing factors, income generation and sustainable livelihoods had the highest mean score of 3.00, while age showed a significant correlation at the 1% level. Factor analysis identified key elements shaping farmers’ opinions on vegetable marketing, including knowledge of preservation, transportation facilities, market accessibility and promotional activities. Major barriers reported by farmers included the high cost of inputs, low profitability, limited access to market information, poor road infrastructure and the high perishability of produce. Furthermore, an assessment of the impact of the educational awareness programme on farmers' knowledge, opinions and marketing behavior indicated a significant improvement with a highly significant change at the 1% level (p < 0.001). These findings highlight the importance of integrating both digital and traditional marketing strategies to enhance market access, improve price realization and promote sustainable agricultural practices. Keywords : Agriculture, Behaviour, Farmers, Marketing, Practices, VegetablesItem Operation Approach of 𝛿𝑃s -Open Sets in Topological Spaces, Fuzzy Topological Spaces and Nano Topological Spaces(Avinashilingam, 2024-11) Shanmugapriya H; Guide - Dr. K.SivakamasundariThe scope of this thesis is to introduce a new operation 𝜚 on the collection of 𝛿𝑃𝑆-open sets of 𝑊, 𝛿𝑃𝑆𝑂(𝑊, 𝜏), into the power set 𝒫(𝑊) of 𝑊 in a way that 𝐻 ⊆ 𝐻𝜚 for every 𝐻 ∈𝛿𝑃𝑆𝑂(𝑊), where 𝒫(𝑊) is the power set of 𝑊 and 𝐻𝜚 is the value of 𝐻 under 𝜚 and to study 𝜚-open sets and all its topological properties. The concept of 𝜚-regular spaces and two types of closures which are 𝑐𝑙𝑜𝑠𝑢𝑟𝑒𝜚, 𝜚-closure of sets are analysed. Some of topological properties on 𝜚-open sets such as limit points, derived sets, neighbourhood, interior, kernel,exterior, boundary, frontier and saturated sets are obtained. The idea of grills is extended to describe a new topology connected to grill 𝜚-space. Similar to the operation 𝜚, another operation 𝜄 on the collection of 𝛿-preopen subsets is introduced and 𝜄-open sets for each of 𝛿𝑃-open set are obtained. Yet another concept of open sets called (𝜄)-open sets using operation 𝜄 for the sub collection 𝛿𝑃𝑆𝑂(𝑊) of 𝛿𝑃𝑂(𝑊) is introduced and relations between 𝜚-open sets and (𝜄)-open sets are discussed. 𝜚-𝑇𝑛 and 𝜚-𝑇′𝑛 for 𝑛 = 0,1,2 are studied with their properties and relationships with other spaces. The theory of generalized closed sets is extended to 𝜚-open sets and 𝜚-generalized closed sets and 𝜚-𝑇1/2 space is defined and its properties are studied. Symmetric spaces, 𝜚-𝑅0 and 𝜚-𝑅1 spaces are introduced with their properties. Two types of continuities 𝛿𝑃𝑆-(𝛼𝜚, 𝛽𝜚)-continuity and (𝛼𝜚, 𝛽𝜚)-continuity are introduced and their properties are focused and compared. Bioperation concepts are introduced for 𝜚-operation. In this connection [𝜚, 𝜚′]-open sets and [𝜚, 𝜚′]-regular spaces are defined and studied. Two types of [𝜚, 𝜚′]-closures are obtained and their properties are discussed. Various spaces like submaximal spaces, extremely disconnected spaces are introduced for bioperation and a characterization theorem is proved via convergence. Some interesting concepts like weakly continuity and somewhat continuity are extended to bioperation. In fuzzy topological spaces and in nano topological spaces 𝜚-open sets are established along with their properties. An edge detection was studied using 𝜚-open sets and Fuzzy 𝜚-open sets. An situation was solved using nano 𝜚-open sets.