Communities in Avinashilingam Institute for Home Science and Higher Education for Women - AULIB-IR Central Library
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All Papers CIA I/EVEN/Common Paper Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
All Papers CIA I/EVEN/M.Sc. Mathematics Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
All Papers CIA I/Even/B.A. English Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
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. Vijayabhanu
The 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.
Assessment of Premenstrual Symptoms among College Faculties A Psychosocial Perspective
(Avinashilingam, 2024-11) Ramya K; Guide - Dr. K. Manimozhi
Premenstrual 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 Balance
All Papers CIA I/EVEN/B.Sc. BC/BT Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
All Papers CIA I/EVEN/BCA Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
All Papers CIA I/EVEN/B.Sc. Computer Science Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
All Papers CIA I/EVEN/B.B.A. RM Feb 2025
(Avinashilingam, 2025-02) Avinashilingam
Agricultural Marketing Behaviour and Practices of Rural Farmers in Dibrugarh District, Assam
(Avinashilingam, 2025-02) Sushmita Deori; Guide - Dr. S. Rajalakshmi
Agriculture 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, Vegetables