Saroja Subbaraj

Curious about which jobs are trending, which skills to learn for a better career, and which job is most relevant to your skills along with your probability of selection? This research paper addresses these questions comprehensively. The first phase employs web scraping techniques to gather data from prominent job websites, constructing a current dataset. This dataset captures emerging job roles, in-demand skills, geographic job distribution, experience requirements, and related insights. In the subsequent phase, a job recommendation system is developed. Leveraging natural language processing (NLP) techniques such as TF-IDF vectorization and cosine similarity, the system matches user-provided skills with job descriptions to recommend relevant job opportunities. Additionally, the recommendation system estimates the probability of selection for each candidate. This project serves as a valuable resource for understanding contemporary job market dynamics, facilitating informed career decisions, and providing insights into emerging job trends and skill requirements crucial for success in the evolving job market.

Blockchain technology has emerged as a pivotal technological innovation with far-reaching implications across various industries. Initially conceptualized by researchers Stuart Haber and W. Scott Stornetta in the early 1990s, blockchain was envisioned as a system to ensure timestamp accuracy and document integrity. The seminal Bitcoin whitepaper, authored by the pseudonymous Satoshi Nakamoto in 2008, introduced a decentralized blockchain-based digital currency system, with the mining of the “genesis block” on 3 January 2009, marking a milestone in its development. Subsequently, the proliferation of cryptocurrencies like Litecoin and Namecoin, based on forks of the Bitcoin network, led to explorations of diverse use cases for blockchain technology beyond its original scope.

As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.

Potable water, commonly known as drinking water, refers to water that is safe to drink and does not endanger human health. It must adhere to strict quality standards set by health organizations, be devoid of dangerous pollutants and chemicals, and meet certain requirements for safety. The health of the public and the ecosystem are directly affected by water quality. Various pollutants have posed dangers to water quality in recent years. A more efficient and affordable approach is required due to the grave effects of low water quality. In this proposed research work, deep learning algorithms are developed to predict the water quality index (WQI) and water quality classifications (WQC), which are vital parameters that can be utilized to know the status of the water. To predict the WQI, a deep learning algorithm called long short-term memory (LSTM) is used. Further, WQC is performed using a deep learning algorithm called a convolutional neural network (CNN). The proposed system considers seven water quality parameters, namely, dissolved oxygen (DO), pH, conductivity, biological oxygen demand (BOD), nitrate, fecal coliform, and total coliform. The experimental results showed that the LSTM can predict water quality with superior robustness and predict WQI with the highest accuracy of 97%. Similarly, the CNN model classifies the WQC as potable or impotable with superior accuracy and a reduced error rate of 0.02. Graphical abstract

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts -dimensional characteristics from the source face image, followed by the feature selection strategy. The face’s distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person’s age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.