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All Journal JOIV : International Journal on Informatics Visualization
Kesavan, Revathi
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Computer Science & IT

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IoT and Deep Learning Enabled Smart Solutions for Assisting Menstrual Health Management for Rural Women in India: A Review Kesavan, Revathi; Palanichamy, Naveen; Thirumurugan, Tamilselvi
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2399

Abstract

A global medical issue, primarily raised in underdeveloped nations, is inappropriate Menstrual Hygiene Management (MHM) among teenage girls. Menstrual hygiene is a global concern because there are over 0.6 billion teenage females (about 8% of the population). The Asian and African continents are home to over 80% of these teenagers. Throughout, 355 million girls and women in India have periods. However, MHM causes discomfort and a lack of respect for millions of women all over the country. In alignment with today's technologies like cloud computing, artificial intelligence (AI), and Internet of Things (IoT), the MHM can be handled effectively. A quantitative survey was carried out among 184 random volunteers aged 18-22 to reveal the current status of MHM in India. The result of the survey confirmed that 72.8% of girls encountered stress during their period, 45% of them were unaware of hygiene products to be used while in the menstruation cycle, 65.2% of them used sanitary pads, and 57.6% of them received disrespectful treatments. This work aims to empower women with the MHM by facilitating knowledge on the menstrual cycle and guiding them about safe-to-use products and disposal strategies in home, work, or community places with the help of technological advancements. Further, introduce a simple friendhood discussion forum through an intelligent chatbot like "Sirona, " a chatbot built over Whatsapp that facilitates a complete ecosystem for MHM.
Adaptive Deep Convolution Neural Network for Early Diagnosis of Autism through Combining Personal Characteristic with Eye Tracking Path Imaging Kesavan, Revathi; Palanichamy, Naveen; Haw, Su-Cheng; Ng, Kok-Why
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3046

Abstract

Autism is a large set of illnesses related to brain development, also referred to as autism spectrum disorder (ASD). According to WHO reports, 1 in 100 children is expected to have ASD. Numerous behavioral domains are affected, including linguistic, interpersonal skills, stereotypical and repetitive behaviors which represent an extreme instance of a neurodevelopmental abnormality. Identifying ASD can be difficult and exhausting because its symptoms are remarkably identical to those of many other disorders of the mind. Medical professionals can improve diagnosis efficiency by adapting deep learning practices. In clinics for autism spectrum disorders, eye-tracking scan pathways (ETSP) have become a more common instrument. This approach uses quantitative eye movement analysis to study attentional processes, and it exhibits promising results in the development of indicators that can be used in clinical studies for autism.   ASD can be identified by comparing the abnormal attention span patterns of children’s having the disorder to the children’s who are typically developing. The recommended model makes use of two publicly viable datasets, namely ABIDE and ETSP imaging. The proposed deep convolutional network consists of four hidden convolution layers and uses 5-fold cross-validation strategy. The performance of the proposed model is validated against multilayer perceptron (MLP) and conventional machine learning classifiers like decision tree (DT), k-nearest neighbor (KNN) and Random Forest (RF) using metrics like sensitivity, specificity and area under curve (AUC). The findings demonstrated that without the need for human assistance, the suggested model is capable of correctly identifying children with ASD.
Co-Authors Ng, Kok-Why Palanichamy, Naveen Su-Cheng Haw Thirumurugan, Tamilselvi