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All Journal JOIV : International Journal on Informatics Visualization Emerging Science Journal
Palanichamy, Naveen
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Computer Science & IT Environmental Science

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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.
A Secure Cloud Service Game Theory Approach to Demand Response Modelling for Residential Users in Smart Grid L, Priya; V, Gomathi; Palanichamy, Naveen
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3541

Abstract

In today's world, electricity has become the keystone for every activity undertaken. As the population increases, the electricity demand has reached unprecedented levels, putting strain on electrical grids. In many developing countries, the residential sector consumes 60% of the peak load. The negative consequences of this trend provide a pathway for frequent brownouts, which lead to enormous losses for industries as well as residential households. To date, the flexibility of energy is usually achieved on the generation side. However, an easier way to counter this would be to manage usage on the demand side. The development of smart grid facilities has enabled communication between utilities and consumers. Therefore, the demand response functionality shows greater potential to stabilize the power supply and demand for the utility and consumers, respectively. In this paper, an intelligent secure cloud service game theory-based demand response modelling algorithm is proposed to handle peak demand in the residential sector. This innovative strategy enables residential consumers to achieve mutually beneficial outcomes. Enhancing communication security between utility providers and consumers, optimizing renewable energy utilization, and improving cost-effectiveness and reliability in electricity production and delivery are vital for meeting the rising demand. The simulation results suggest that the proposed approach efficiently reduces the Peak-to-Average Ratio, leading to mutual advantages for both consumers and utility providers. This approach addresses the growing demand for electricity while promoting sustainable energy through improved energy management practices.
Continuous Training of Recommendation System for Airbnb Listings Using Graph Learning Chan, Yun Hong; Ng, Kok Why; Haw, Su Cheng; Palanichamy, Naveen
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2315

Abstract

Recommender systems are getting increasingly important nowadays as they can boost user engagement and benefit businesses. However, there remain some unsolved problems. This paper will address two key performance issues. First, the limited ability to identify and leverage intrinsic relationships between data points. Second, the inability to adapt to new data. The first issue is proposed to be addressed through a Graph Neural Network (GNN) to curate better recommendations. GNN will be trained with Airbnb’s review data to utilize its outstanding expressive power to represent complex user-listing interactions at scale, followed by generating embeddings to compute the relevant recommendations to the users. With the generated embeddings, the recommender system will compute a recommendation list to every user based on the embedding similarity between the user and listings or the user’s first-ever reviewed listing and listings. The second issue is proposed to be resolved by incorporating Continuous Training. The proposed recommender system employs GraphSAGE with a customized Rating-Weighted Triplet Ranking Loss function, which outperformed unsupervised GraphSAGE. Offline simulation validated the recommender system's ability to learn from the latest data and improve over time. Overall, the proposed user-to-item (U2I) recommendation rating-weighted GraphSAGE substantially increased by 99.88% in hit-rate@5 and 98.15% in coverage. This offers an effective solution for enhancing the recommender system for Airbnb listings. This research validates the efficacy of GNN-based recommendations in capturing user-item relationships to aid in predicting relevant recommendations, thus significantly driving up the adoption of GNN-based recommender systems.
LRX: A Hybrid-based Real-Time Air Quality Index Prediction and Visualization Model Jayapradha, J.; Haw, Su-Cheng; Palanichamy, Naveen; Arunesh, V.; Pranav, Surajith; Senthil Kumar, T.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-010

Abstract

Accurately predicting the air quality index significantly reduces health risks and supports urban environmental planning. This paper presents LRX, a hybrid predictive model, for Air Quality Index (AQI) prediction. The model employs Long short-term memory to capture temporal dependencies, Random Forest to fine-tune the features, and Extreme Gradient Boosting to enhance the final predictions. The objective of the study is to build a model that can accurately predict air quality index numbers in real time for many cities in India. The proposed model LRX design influences the depth of each algorithm to enhance accuracy and generalization. The experimental results show the model's ability to predict the AQI forecast of various cities in India with a root mean square error of 0.014 and R2 of 0.948, performing better compared to the models individually. To enhance this, a Stream lit-based user interface has been developed to enable real-time AQI predictions and visualization. The interface incorporates tabs for interactive inputs, model selection, graphical representation of predicted trends, ensuring accessibility and usability, and enhancing the practical applicability of the proposed model. This easy-to-navigate tool not only makes the prediction process more accessible but also helps bridge the gap between complex model results and practical environmental decision-making, enhancing the overall impact of the research. This research contributes to air quality prediction by presenting a robust modelling approach that can be applied in the real world.
Co-Authors Arunesh, V. Chan, Yun Hong Haw, Su Cheng Jayapradha, J. Kesavan, Revathi L, Priya Ng, Kok Why Ng, Kok-Why Pranav, Surajith Senthil Kumar, T. Su-Cheng Haw Thirumurugan, Tamilselvi V, Gomathi