Jayavel Amudhavel

The data science and artificial intelligence, optimizing information retrieval tasks has become crucial for extracting actionable insights from vast amounts of data. The problem is the need for precise query formulation to retrieve relevant data effectively, as LLMs can generate vast amounts of information that might include noise or irrelevant details. The objective of this study is to enhance the efficiency and accuracy of information retrieval tasks by leveraging large language models (LLMs) for data augmentation. Gathering a diverse dataset from various sources like online search engines, social media platforms, and online forums is crucial for meeting text and information needs effectively. The term frequency-inverse document frequency (TF-IDF) technique is applied to calculate the importance of each term in the dataset, allowing for the differentiation of significant words from common ones. This step is crucial in the data pre-processing phase to enhance the relevance and precision of information retrieval tasks. The goal is to optimize the normalized discounted cumulative gain (NDCG) metric, which measures the ranking quality of the retrieved documents. The framework integrates the fine-tuned LLM model with the chosen information retrieval system by incorporating the model’s outputs into ranking algorithms. The results show that the proposed method has the maximum accuracy, with an average accuracy of around 10 % when implemented using Python software. The future scope for optimizing information retrieval tasks with large language models (LLMs) for data enhancement is vast and promising.

Indirect-imaging methods involve at least two steps, namely optical recording and computational reconstruction. The optical-recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical-modulation functions and reconstruction methods developed in the past few years for different applications. In most cases, a compatible pair of the optical-modulation function and reconstruction method gives optimal performance. A new reconstruction method, termed nonlinear reconstruction (NLR), was developed in 2017 to reconstruct the object image in the case of optical-scattering modulators. Over the years, it has been revealed that the NLR can reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR isinvestigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed.

Indirect imaging methods involve at least two steps, namely optical recording, and computational reconstruction. The optical recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical modulation functions and reconstruction methods developed in the past years for different applications. In most cases, a compatible pair of optical modulation function and reconstruction method gives optimal performance. A new reconstruction method termed non-linear reconstruction (NLR) was developed in 2017 to reconstruct the object image in the case of optical scattering modulators. During the years, it was revealed that the NLR could reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR has been investigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed.

Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16).

Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is a strong demand for the prediction of its severity using non-invasive methods. The primary data containing serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age. The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD. The study aims to compare and evaluate different machine learning models in the prediction of severity in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure, Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar’s test was conducted to validate the empirical results which is a statistical test. The final objective is to identify the best machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost (AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar’s statistical test results support that the RF classifier outperforms than the other classifiers.

VANET information dissemination among vehicle to infrastructure and infrastructure to the vehicle is necessarily more important. Sharing resources between vehicles such as safety messages, weather reports, multimedia applications, traffic information's, alert message without any failures is still an issue in VANET.A Lot of improvements needed for resource sharing by proper load balancing. Distributed Spanning Tree is similar to broadcast algorithm. The broadcast algorithms are used to send the messages. To analyze the function of Load balancing which is an unsolved problem in vehicular networks due to the movement of vehicles. Distributed Spanning Tree structure is proposed to frame the fault-tolerant load balancing in VANET. The main aim of load balancing is to distribute resources for increasing the performance and to minimize the delay. This fault-tolerant load balancing is achieved by Distributed Spanning Tree (DST). Intelligent transportation system makes communication more robust and safe between the vehicles. Vehicular Ad-Hoc Network is gaining popularity in the research field due to increase in safer communication between vehicles. Load balancing in vehicular networks is more critical due to mobility of vehicles. Discovery of services in VANET which ensures good QoS (Quality of Service) is very essential.

Computing consists of a network of heterogeneous computers, from which a virtual super computer is essentially formed. It displays immense potential as the various resources across large networks can be pooled to service many and be utilized by many, using the Internet from around the world. The potential for parallel CPU processing is one of the most attractive features of a grid. A perfectly scalable application will finish five times faster if it uses five times the number of processors. Application software as required by the users of the grid. Thus, the structure can be represented in layers, as implied by the grouping of grid components. Hardware, the bottom layer, would then contain a large number of heterogeneous resources and would be accessed by a limited number of users to ensure data privacy. The next layer would then consist of application software and tools that are useful for the users and which are domain-specific. In this research we analyzed the distributed and high-performance system in grid computing to provide the efficient resource discovery and message broadcasting. The Distributed Spanning Tree (DST's) implementation is altered and adapted to achieve better server load and message load distribution by a selective search and look-up mechanism in this proposal. In addition, a fault tolerance mechanism is also expressed in this contribution, as part of the DST's adaptation, such that if the system which is providing the service fails or leaves the grid environment, then the backup site will immediately take up the execution and recover the task.