Ph.D Theses

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    A Comparative Study of Mythology in the Novels of Narendra Kohli and Amish Tripathi (With Special Reference to Ram Katha Series)
    (Avinashilingam, 2024-06) Niraja T K; Dr. G. Shanthi
    ममथक दुननया के सबसे समृद्ध ऩौराणिक कहाननयों का एक खजाना है । इसका अद्भु त एवॊ अनूठा ऩहऱू यह है कक सभी ऱोग अऩने देश या समाज के इन सददयों ऩुरानी कहाननयों से ऩूिण रू ऩ से ऩररचित है । इन कहाननयों को हमे बिऩन में सुनाया जाता है जो कऱ के मऱए एक अच्छे नागररक बनने के मऱए हमारे व् यक्ततत्व को आकार देता है । इसके साथ ममथक ककसी व् यक्तत से सॊबॊचधत ववरासत या सॊस् कृनत का ननमाणि में अहम भूममका ननभाती है। ममथक और ऱोक कथाएॉ ऱोगों की धमों का आधार बन जाती है क् जनका वे सददयों से ऩाऱन करते आए हैं ।ममथकीय कहाननयों में जो बुराई और अच्छाई के बीि ऱडाई उल्ऱेणखत है उससे हमें नैनतक मूल् य सीखने को ममऱता है । ममथक ऩूवणजों के मऱए महत् वऩूिण थी ,आजभी महत् वऩूिणहैऔरहमेशा रहेगी। इसका प्र भाव इतना सशतत है कक कई मानवीय तकों में भी ममथक का प्र नतबबॊब छायाॊककत होता है । ममथक को महत् वऩूिण मानने के ऩीछे का सबसे मुख् य कारि यह है कक यह एक ऐसी कहानी है क् जसमें कुछ वास्तववक तथ्य शाममऱ है । एक ही ममथक में ववश्वास करने वाऱे ऱोगों के बीि कभी -कभीएकताभी होताहै । इनमें मतभेद होने की सॊभावना कम ददखाई देता है । ममथक को अऩने आऩ में यथाथण माना जाता है जो मनुष् य के अॊदर कृतज्ञ एवॊ आशा की भावना को जगाती है । इसी कारि से ममथक का मनुष् य ऩर प्र भाव के सॊबॊध में ताककण क रू ऩ से विणन करना कदठन है । ममथकीय सादहत्य के ऱेखन में ववमभन्न रिनाकारों ने अऩना महत् वऩूिण योगदान ददया है । उनमे नरेंद्र कोहऱी एवॊ अमीश बिऩाठी के ममथकीय उऩन्यासों ऩर मैंने शोध कायण ककया है ।
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    A Deep Learning Framework for Detection and Segmentation of Multiple Artefacts in Endoscopic Images
    (Avinashilingam, 2023-05) Kirthika N; Dr.B.Sargunam
    Endoscopy is a standard procedure for disease surveillance, monitoring inflammations, detect cancer and tumor. During the procedure the organs are visualized. Artefacts, an artificial effect is found to be present in the resultant images. They play a dominant role in increasing procedure time by more than an hour. Hence an efficient algorithm to detect, segment and restore could assist clinician. The artefacts present in an endoscopic image include saturation, specular reflections, blur, bubbles, contrast, blood, instruments and miscellaneous artefacts. The presence of these artefacts acts as a barrier when investigating the underlying tissue for identifying clinical abnormalities. It also affect post processing steps where most of the images captured are discarded due to the presence of artefacts which in turn affects information storage and extracting useful frame for report generation. Endoscopic artefact detection dataset is the only available public dataset holding endoscopic images with annotations for multiple artefacts. Hence, a custom dataset is annotated using the same annotation protocol of endoscopic artefact detection dataset to maintain homogeneity. The algorithms are trained and tested with images from both public and custom dataset for artefact detection. State of the art object detection algorithms such as YOLOv3, YOLOv4 and faster R-CNN are used for detecting artefacts in endoscopic images. The detection algorithm focusses on three important performance parameters namely mean average precision, intersection over union and inference time. The ensemble model outperformed well across all the performance parameters compared with literature. The inference time is reduced by 8.63%, whereas the mAP and IoU are increased by 61.67% and 63.47% respectively. newlineSegmentation algorithms like U-Net with EfficientNetB3 backbone, Link-Net with EfficientNetB3 backbone and U-Net with SE-ResNeXt101 backbone are used to segment the artefacts. The results are assessed with performance parameters like F2 score and Jaccard score.The results proves a phenomenal increase in Jaccard score by 17.36% and F2 score by 17.42% respectively. An image if found to have artefacts after artefact detection,the affected region will be segmented by the proposed segmentation algorithm.To visualize the scope and need of artefact detection and segmentation a simple application is developed to restore the artefacts. The segmented output contains a binary mask using which fast marching algorithm will restore the segmented area. Hence the resulting restored image gives the clinician a better view of the organ. A simple CNN based classifier is proposed to classify polyp. It is found that the classifier's performance is improved by 3.09% when the artefacts in the images are restored. Thus such outcomes when implemented in real time could effectively have a control over the false diagnosis rate, which is the rate at which the disease is misclassified, procedure time and clinician's fatigue as well.
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    A study on Neuroprotective potential of in vitro and field tissues of Withania somnifera using Caenorhabditis elegans model
    (Avinashilingam, 2023-03) Krishnapriya C; Dr. Kalaiselvi Senthil
    Withania somnifera is a prevalent medicinal herb used all over the world as a domestic remedy for addressing several age-related ailments. The plant is also one of 32 medicinal plants that have been ranked as priority medicinal plants by the National Medicinal Plant Board (NMPB). Ayurveda refers the field grown W. somnifera roots as a Rasayana medication (Rejuvenator). It has been used as the major ingredient in a variety of formulations to help slow down the aging process, cope with stress, and be an excellent neuroprotectant. However, the quality and quantity of traditionally cultivated plants present a significant obstacle to their utilization in herbal formulations. This study aims to demonstrate that in vitro shoot tissues of W. somnifera could be used as an alternative and be as bioactive as roots grown in the field. The HPTLC quantification of major withanolides and GC-MS profiling of metabolites revealed that the pharmacological actives of IS (in vitro shoot) showed the overall similar metabolite profile as in FR (field grown roots). As measured by DPPH radical scavenging activity, the antioxidant potential of in vitro shoots (IS) was also higher than that of field grown tissues (FR & FS) and in vitro roots (IR). The animal model study in Caenorhabditis elegans presented numerous lines of evidence regarding the effectiveness of the IS on the health and life expectancy over the FR, IR and FS. Along with this, the study compares the molecular level mechanisms underlying the beneficial effects of FR, IR, FS and IS supplementation by using gene-specific mutants. The efficacy of W. somnifera extracts to prevent α-synuclein aggregation, its associated pathologies, and its capability for neuroprotection were studied in Parkinson’s disease-modeled worms. The finding of this study highlighted that IS is equally bioactive as traditionally used FR. Moreover, the IS extracts efficiently prolongs the lifespan, heath span and stress resistance via insulin/insulin- like growth factor-1 (IGF-1) signaling (IIS) and mitochondrial electron transport chain complexes (mETC). The IS extract is more effectual for suppressing oxidative stress, a remarkable neuroprotectant in Parkinson’s disease modeled worms. As the first study to investigate the bioactivity of W. somnifera shoots cultivated in vitro, these results could contribute to the scaling up of IS culture systems and in vitro shoot tissues for treating neurological and age-related ailments, extending patients' lives, and improving their quality of life.
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    Acceptability and Supplementation of Red Palm Oil on Selected Target Groups
    (2007-01-06) Thirumani Devi, A; Amirthaveni, M
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    Acceptability of Soya Based Recipes in Food Service
    (1995-07) Sarojini, K S; Parvathi Easwaran, P
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    Accessibility and Adaptability of Limb Prosthesis - An Ergonomic Concern
    (2015-07) Sarasvathi, V; Visalakshi Rajeswari, S
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    Adhunik Dalit Kahaniyon Mein Stri Chetana (Chunee Huyi Kahaniyon Ke Vishesh Sandarbh Mein)
    (Avinashilingam, 2024-11) Arunima A M; Dr. Shobhana Kokkadan
    बायतीम साहहत्म भें प्र ािीन साहहत्म से रेकय हहॊदी साहहत्म तक दशरत िगत का धित्रर् फडे ऩैभाने ऩय देखने को शभरता है। इस सम् ऩ र् त बायतीम साहहत् म भें छुआछत की ऩीडा, दशरत भहहराओॊ की सभस्मा औय उनके शोषर् िैसे अन्मामों के प्र तत आक्रोश औय विद्रोह की अशबव् मजक् त हुई है। हिायों िषों से धभत , शास्त्र , ऩयॊऩया औय यीतत-रयिािों के नाभ ऩय उनका खफ शोषर् ककमा गमा है। अनेक प्र काय की अऺ भताओॊ के कायर् न केिर उनकी ऩहिान नगण्म यही है , फजकक उन्हें हभेशा गयीफी की खाई भें धकेरा गमा है , िो सैकडों ऩीह़िमों से िानियों से बी फदतय िीिन िीने को भिफ य हैं, धाशभतक औय ऩायॊऩरयक रोगों द् िाया दशरतों का हय सॊदबत भें शोषर् ककमा िाता यहा है। िषत १९९० के दशरत साहहत्म ऩय निय डारें तो मे िे फदराि थे िफ साभाजिक , आधथतक, यािनीततक औय साहहजत् मक ऺेत्रों भें प्र गतत हुई। दशरत साहहत्म १९९० से ऩहरे बी शरखा गमा था रेककन दशरत भहहराओॊ की सभस्माओॊ को उस तयह से नहीॊ उठामा गमा जिस तयह से १९९० के फाद उनकी सभस्माओॊ को उठामा गमा। बायतीम साभाजिक व् मिस् था भें दशरत भहहराओॊ की जस् थतत फहुत दमनीम है। दशरत भहहराएॊ दोहये अशबशाऩ से ग्रस् त हैं। ए क तो भहहरा होने का औय दसया दशरत भहहरा होने का। दशरत भहहराओॊ का रेखन औय दशरत भहहराओॊ को केंद्र भें यखकय ककमा गमा रेखन भुख् म रू ऩ से आधुतनक मुग की देन है। दशरत स्त्र ी िेतना सभाि औय साहहत्म भें दशरत भहहराओॊ के फ़िते शोषर् का ऩरयर्ाभ है। बायतीम सभाि भें हभेशा से ही िातत के आधाय ऩय भहहराओॊ के फीि बेदबाि होता यहा है। िातत आधारयत शोषर् के कायर् दशरत भहहराओॊ के अजस् तत् ि को हभेशा से नकाया गमा है। उन् हें ऩ ॊिीिादी सभाि द् िाया उऩबोग की िस् तु की तयह खयीदा औय फेिा िाता यहा है। दशरत भहहराएॊ इॊसान होते हुए बी िानियों की तयह िीने को अशबशप्त हैं। सभाि भें अऻानता के अॊधकाय के कायर् उनका हय रू ऩ भें शोषर् होता था। दशरत सभाि को सभाि भें अऩनी ब शभका स् थावऩत कयने के शरए हय कदभ ऩय सॊघषों का साभना कयना ऩडा। सभकारीन हहॊदी दशरत कथा साहहत्म ऩय िफ हभ निय डारते हैं तो मह फात साप तौय ऩय निय आती है। दशरत साहहत्मकायों ने आठिें दशक भें ही दशरत साहहत् म भें अऩनी उऩजस् थतत दित कयानी शुरू कय दी थी। अऩनी यिनाओॊ के भाध्मभ से मे रेखक उस सभाि की सच्िाई को रगाताय जिक्र कयने रगे िो सहदमों से दफा यहने के शरए अशबशप्त था। िमतनत ‘दशरत’ कहातनमों भें साभाजिक, यािनीततक, साॊस् कृततक, आधथतक, धाशभतक ऩरयप्रेक्ष् म भें फदरते हुए िीिन सॊदबत भें दशरत स्त्र ी ककस प्र काय की िुनौती साभना कय यहा है। इसका विश्रेषर्ात्भक अध्ममन ककमा गमा है। िततभान ऩरयदृश्म भें सॊिैधातनक अधधकायों औय िागरूकता अशबमानों के कायर् बायतीम साभाजिक सॊयिना भें कापी फदराि देखने को शभर यहे हैं। रेककन सभाि भें अबी बी स्त्र ी -ऩुरु ष, दशरत औय गैय-दशरत, अभीय औय गयीफ, ग्र ाभीर् औय शहयी , कारे औय गोये के फीि गहयी खाई है। दशरत भहहराओॊ की हारत फद से फदतय है। हाराॉकक, दशरतों औय भहहराओॊ की जस् थतत भें सुधायात्भक फदराि देखे िा यहे हैं। रेककन िातत , अऩभान, ततयस्काय , शोषर् औय उत् ऩीडन की भिफ त दीिाय को ऩ यी तयह से तोडने भें सभम रगेगा, रेककन अच्छी खफय मह है कक अफ दशरत रेखकों औय भहहरा रेखकों द् िाया दशरत भहहरा िीिन की बमािह त्र ासदी को सभाि के साभने राने औय उससे फिाने के शरए एक सॊमुक् त प्र मास ककमा िा यहा है। रेककन दशरत भहहराएॊ अबी बी ऩ र् त रू ऩ से ऩुरु षों की तयह िागरू क औय भिफ त नहीॊ हो ऩाई हैं। बायत की िततभान साभाजिक सॊयिना भें इन दशरत भहहराओॊ की दमनीम जस् थतत औय उससे भुजक् त के उनके प्र मासों का विश्रेषर् कयने औय रोगों को सभझाने के शरए भैंने 'आधुतनक दशरत कहातनमों भें स्त्र ी िेतना (िुनी हुई कहातनमों के विशेष सन् दबत भें) विषम ऩय शोध कयना उधित सभझा। अध् ममन की सुविधा के शरए शोध-प्र फॊध को कुर ऩाॉि अध्मामों भें विबक्त ककमा गमा है - प्र थभ अध्माम - 'दशरत स्त्र ी िेतना एक अध्ममन ' भें ‘िेतना’ शब्द , अथत, ऩरयबाषा, दशरत का ऩरयिम, दशरत स्त्र ी की अिधायर्ा , दशरत साहहत्म , बायतीम दशरत साहहत्म , कथा - साहहत्म के विशबन्न ऩरयदृश्म - ऩय प्र काश डारा गमा है। द् वितीम अध्माम - 'हहन्दी की प्र भुख दशरत कहातनमाॉ एिॊ स्त्र ी ऩात्र ' शीषतक के अॊतगतत आधुतनक दशरत कहातनमाॉ एिॊ स्त्र ी ऩात्रों का विश्रेषर् तक का अध्ममन ककमा है। दशरत स्त्र ी ऩात्रों ऩय सॊघषत , ऩीडडत दशरत स्त्र ी , प्र ततयोध के आिास फनी दशरत स्त्र ी , तनडय दशरत स्त्र ी का विशबन् न ऩहरुओॊ ऩय अध् ममन ककमा गमा है। तृतीम अध् माम - ‘दशरत स्त्र ी कहातनमों भें अजस्भ ता फोध एिॊ िीिन सॊघषत - िुनी हुई कहातनमों के सन्दबत भें ’ इसभें दशरत स्त्र ी अजस्भता , िीिन सॊघषत की साभाजित, आधथतक, साॊस् कृततक- धाशभतक ऩरयप्रेक्ष् म का विस् तृत अिरोकन ककमा गमा है। साथ ही िेतनाऩयक आन्दोरनों से प्रे रयत दशरत जस्त्रमों द् िाया अऩनी अजस्भता को ििद भें यखने के सॊघषतऩयक प्र मास का स क्ष् भ ऩयक अध्ममन ककमा गमा है। चतुथभ अध् माम का शीषतक है - 'दशरत स्त्र ी िेतना एिॊ प्र ततयोध िुनी हुई कहातनमों के सन्दबत भें । प्रस् तुत अध् माम भें प्र ततयोध का ऐततहाशसक ऩरयदृश्म , दशरत स्त्र ी एिॊ प्र ततयोध का साभाजिक, आधथतक, यािनैततक, धाशभतक, साॊस् कृततक ऩरयप्रेक्ष् म का विश् रेषर् ात् भक अध्ममन ककमा गमा है। इसभें दशरत भहहराओॊ के प्र ततयोध के विशबन्न रू ऩ अॊककत ककमा गमा है। ऩॊचभ अध्माम - 'आधुतनक दशरत कहातनमों का शशकऩ एिॊ बाषा िैशशष्ट् म' भें िुनी हुई दशरत कहातनमों को शशकऩगत िैशशष्ट्म एिॊ बाषा िैशशष्ट्म के आधाय ऩय विबाजित ककमा गमा है। शशकऩ, शशकऩ का अथत, ऩरयबाषा, स् िरूऩ , शशकऩगत िैशशष्ट् म के अॊतय विषमिस् तु, कथानक, िरयत्र धित्रर् , देशकार तथा िाताियर् , शीषतक का प्र मोिन , बाषा शैरी को अनुच् छेदों भें वििेधित ककमा गमा है। बाषा शैरी भें िर्तनात्भक शैरी , आत्भकथा शैरी , स् िप्न शैरी , सॊिाद शैरी, धित्रात्भक शैरी , नाटकीम शैरी, पैटैसी आदी शैरी का वििेिनात् भक अध् ममन प्रस् तुत ककमा गमा है। आधुतनक दशरत कहातनमों की बाषा िैशशष्ट्म भें शब्द िमन के अॊतय तत्सभ तत्बि , देशि शब्द , अॊग्रेजी , अयफी पायसी को सजम्भशरत ककमा गमा है। अध्मामों के फाद ‘उऩसॊहाय’ शीषतक के अन्तगतत अध्ममन -विश्रेषर् के साय सॊऺेऩ के साथ-साथ अध् ममन का तनष् कषत प्रस् तुत ककमा गमा है। उसके फाद ‘सॊदबत ग्र ॊ थ स िी’ भें अध् ममन के शरए प्र मुक् त आधाय ग्र ॊ थों एि सहामक ग्र ॊ थों का ऩरयिम हदमा गमा है। अध् ममन के शरए उऩमोगी ऩत्रत्र काओॊ औय िेफसाइट की स िी बी तदनन् तय सभाहहत की गमी है। अॊत भें ऩरयशशश्ट के अॊतगतत अऩने प्र काशशत शोध आरेख औय ‘प्रािारयसभ रयऩोटत ’ सॊक्न है। इस शोध प्र फॊद को अऩनी ऺ भता के अनुसाय त्रु हटहीन फनाने का बयसक प्र मास भैंने ककमा है। कपय बी महद कोई त्रु हट मा कभी शेष यही है तो उसके शरए भैं ऺ भाऩाथी हॉ।
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    Adolescents of Arunthathiyar Population -An Exploratory Study
    (2018-08) Jahnavi Devi, S; Arockia Maraichelvi, K
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    Air quality Prediction using Deep Learning Techniques
    (Avinashilingam, 2024-06) Shree Nandhini P; Dr. P.Amudha
    Machine Learning models and Deep Learning models have been widely used to predict the air quality. Monitoring air quality involves both regulatory measures and public awareness campaigns to reduce emissions from various sources such as vehicles, industrial activities, agriculture, and household combustion. Air pollution predict is very useful for informing about the pollution level that allow policy makers to adopt measures for reducing its impact. Over the past few decades, due to human activities, industrialization, and urbanization, air quality condition has become a life-threatening factor in many countries around the world. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the PM2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution. Air pollution refers to the presence of harmful or excessive quantities of substances in the air we breathe, which can be detrimental to human health, the environment and ecosystems. These substances, known as pollutants, can come from various sources, including industrial activities, vehicle emissions, agricultural practices, and natural phenomena. Common air pollutants include particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), volatile organic compounds (VOCs), and ozone (O3). The air quality prediction is used to predict the future state of air quality in a particular location based on the existing data, such as historical air quality data. In the first phase of the research work, an Improved Sparse Auto Encoder-Deep Learning Algorithm (ISAE-DL) is used to predict the air quality system and the feed forward neural network is utilized as a sparse auto encoder. The combined k-Nearest Neighbor Euclidean Distance (kNN-ED) and kNN- Dynamic Time Warping Distance (kNN-DTWD) is used to acquire the particulate matter and the meteorological data. In addition, Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) are used to acquire the relative information and the classification model is generated with training data. In the second phase of research work, Voronoi Clustering Sparse Auto Encoder- Deep Learning (VCSAE-DL) is developed to handle the long time delay based locations for better air quality prediction. Then, the temporal and spatial features are identified to retrieve the most important features for air quality prediction. The formation of clusters is continued with different centers and the clustering process is stopped until all the data are covered. The clustered data and the terrain information are given as input to the Neural Network layer such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short- Term Memory (LSTM) and their results are combined and transferred to Sparse Auto encoder for the prediction of air quality. This method efficiently reduces the long-term delay issues, but this method can also suffer to learn from the long-term dependencies of air pollutant concentrations. In the third phase of research work, a Transferred Stacked Bidirectional and Unidirectional Long Short-Term memory (T-SBU-LSTM) is proposed to minimize the long term dependencies for LSTM for air quality prediction. Then, the Transferred Stacked Bidirectional and Unidirectional LSTM (T-SBU-LSTM) was adopted in learning from long- term PM2.5 dependencies, and it uses Transfer learning to transfer knowledge from smaller temporal resolutions to higher temporal resolutions. Transfer learning is used to improve prediction accuracy at higher temporal resolutions which identifies the similarities between two separate datasets, tasks, or models to transmit data from the source to the new domain. This combined architecture enhances the feature learning from the large-scale spatial- temporal time series data by learning both forwards and backward dependencies. This phase of research expands the air quality prediction from a specific location to several adjacent locations varying small period to long period time delays. In the fourth phase of work, Wasserstein Distance - Deep Transfer Learning (WD- DTL) is proposed to reduce the learning time of Transfer Learning. Then, the Wasserstein distance based Deep Transfer Learning (WD-DTL) is constructed to learn invariant features between source and target domains. Initially, a base LSTM model is trained with sufficient data in source domain. Finally, the developed approaches like Improved Sparse Auto Encoder Using Deep Learning (ISAE-DL), Voronoi Clustering Sparse Auto Encoder Using Deep Learning (VCSAE-DL), Transferred Stacked Bidirectional and Unidirectional Using Long Short Term Memory Algorithm (T-SBU-LSTM) and Wasserstein distance using Deep Transfer Learning (WD-DTL) based air quality prediction system were compared using the performance metrics, Accuracy, Precision, Specificity, Sensitivity, AUC, MCC and MAER. The experimental results proved that WD-DTL based air quality prediction system accomplishes better than the other prediction algorithms.
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    An Analytical Study on the Compositions of the Renowned Violinist Thrissur C Rajendran
    (Avinashilingam, 2024-11) Sruthy K; Dr.V.Janaka Maya Devi
    Composers play a vital role in Carnatic Music for its evaluation and founding a structure for it. Composers are thus essential for both the educational and artistic frameworks of Carnatic Music. Their contributions to develop and mold this field are immense, particularly during main three periods like, the Trinity period, before and after the Trinity period. Before the Trinity period, the composer Purandara Dasa, the “Father of Carnatic Music” created a fundamental teaching methods and many compositions, that are still remain an indispensable for leaning part in Carnatic Music. Tyagaraja, Muthuswamy Dikshitar and Syama Sastri – the trinities of Carnatic Music, established the main musical form Kriti, that emphasizes devotion and artistry with fusing melodic complexity and lyrical meaning. The composers came after this period also followed this tradition and contributes a lot in to this filed. In this, the contributions of modern composers and their innovations are highly appreciable and inseparable. Many modern composers composed many compositions in various musical forms, with several new innovations and experiments, without breaking the traditions of their ancestors. These compositions are widely staged in the concerts by the young enthusiastic artists of Carnatic Music. But then also, some talented composer’s compositions are not coming out much and they need to be staged more also. Thrissur C.Rajendran is one of the composers from this list, who is a well- known Violinist from Kerala, composed many Carnatic compositions in various musical forms like Varna, Kriti, Ragamalika, Padam, Bhajan, Tillana and Mangalam. He is living legend, who composed 70 compositions with high order of musical and lyrical excellence. This thesis is entitled the “An Analytical Study on the Compositions of the Renowned Violinist Thrissur C.Rajendran” is divided into four chapters excluding Introduction and Conclusion. The first chapter describes the life and contributions of Thrissur C.Rajendran in to the Music world. The second chapter deals with ragas utilized in C.Rajendran’s compositions and categorised in to different heads. The third chapter elucidates the significances of Kshetra compositions of C. Rajendran. In the fourth, analysed the compositions of C.Rajendran in a Concert Paddhati. This thesis mainly focuses the compositions of Thrissur C.Rajendran and explored the musical excellence, with Raga bhava through the embellishments of svaras, Gamakas, Sangatis, Decorative angas, the rhythmical excellence, the rhetorical beauties in the lyrics and also the significances of Kshetra compositions.