Comparative studies on inducers in the production of naringinase from Aspergillus niger MTCC 1344

This research provides detailed systematic study of the effect of different inducers (hesperidin, naringenin, naringin, rhamnose and rutin) in naringinase production by Aspergillus niger MTCC 1344. Cultures were carried out in shake flasks and they produce extracellular naringinase in a complex (molasses, peptone and salts) medium. The optimized concentration (%) of naringin, rhamnose, naringenin, rutin and hesperidin for   maximized naringinase production are 0.1, 0.375, 0.01, 0.2 and 0.2, respectively. Compared with control,  inducers increased the naringinase production by many folds in the order of naringin (6.63) > rhamnose (4.87) > naringenin (3.26) > rutin (2.84) > hesperidin (2.35). Under optimum conditions, about 9.68 units of enzyme per ml complex medium containing naringin were obtained on the 7th day. The activity to inducer (A/I) ratio was 968 Ug-1 naringin, and the cultivation time was shorter in submerged production. The results indicate that naringinase activity used naringin as an inducer which was significantly higher than the other four inducers. Therefore naringin is recommended for naringinase production.Key words: Naringin, naringenin, rutin, hesperidin, rhamnose, naringinase, Aspergillus, inducer, molasses

Clustering Techniques for Recommendation of Movies

A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated

Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD

Aerodynamic Design Improvement for an Intercity Bus

Intercity buses travel about 250 to 350 km in a stretch and usually are of sleeper coach mode. The exterior styling, sleeper comfort and aerodynamically efficient design for reduced fuel consumption are the three essential factors for a successful operation in the competitive world. The bus body building companies prioritizes the exterior looks of the bus and ignore the aerodynamic aspect. Scientific design of sleepers for increased comfort of the passengers is seldom seen. The overall aim of this project was to redesign an intercity bus with enhanced exterior styling, reduced aerodynamic drag and increased comfort for the passengers. Principles of product design were used to analyze the styling and comfort. The benchmarked high floor bus was redesigned with low - floor for reduced aerodynamic drag. The exterior was redesigned with emphasis on improvised aerodynamic performance and appealing looks. The interior was modified to meet aspirations of the commuters. The results of the redesigned exterior body showed a reduction of about 45% in coefficient of drag and overall aerodynamic drag reduction by 60% due to combined effect of reduced coefficient of drag and frontal area

An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson’s Disease

People with Parkinson’s Disease (PD) might struggle with sadness, restlessness, or difficulty speaking, chewing, or swallowing. A diagnosis can be challenging because there is no specific PD test. It is diagnosed by doctors using a neurological exam and a medical history. This study proposes several Machine Learning (ML) algorithms to predict PD. These ML algorithms include K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting algorithms (XGBoost), and their ensemble methods using publicly available PD dataset with 195 instances. The ML algorithms are used to predict and classify PD using homogeneous XGBoost ensemble techniques with reduced amount of entropy. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle imbalanced data, and 10-fold cross-validation is employed for evaluation. The results show that the homogeneous XGBoost-Random Forest outperforms other ML methods with 98% accuracy and Matthew’s correlation coefficient value 0.93

PrEGAN: Privacy Enhanced Clinical EMR Generation: Leveraging GAN Model for Customer De-Identification

Privacy in medical records while data sharing is a major concern for distributed learning models. The dataset generated and shared via Electronic Medical Records (EMR) consist of sensitive medical information such as patient identify and experts recommendations, and causes setbacks in training larger models, dataset augmentation and polluting datasets with recursive attributes. The information processing and de-identification is proposed in this article to preserve and enhance the privacy of EMR. The proposed technique is termed as PrEGAN (i.e.) Privacy Enhanced Generative Adversarial Network (GAN) for EMR data training and realistic mapping. The proposed model generates and discriminates the ground truth with generated mask via a computation of loss function for de-identification or removal of personal linked/connected data in the records networks. The objective is to generate the mask of EMR, which is realistic and similar to the ground truth. The model is trained and validated with two distinguished discriminators, the CNN based discriminator is used for medical images, whereas Neural Networks are used for textural data generator. The experimental results demonstrate a higher degree of data privacy and de-identification in EMR with 88.32% accuracy in predicting and eliminating via RoI and loss function

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen’s Kappa value. These indicators highlight the model’s proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information

Green Campus Audit Procedures and Implementation to Educational Institutions and Industries

Nature provides a free lunch, but only if we control our appetites. As we are in the twenty-first century, modernization and industrialization are the two important outputs that have made human life more luxurious and comfortable. Simultaneously, they are responsible for several uses of exploitation of forests, natural resources, and wildlife, polluting the scarce, producing massive solid waste and sacred water resources, and finally making our planet Earth ugly and inhospitable. Today, people are getting more familiar with global issues like global warming, the greenhouse effect, ozone depletion, climate change, etc. Now, it is considered a final call by Mother Earth to walk on the path of sustainable development. The time has come to wake up, unite and combat together for a sustainable environment. The present study focuses on the concept of green audit and its importance with respect to the conservation of nature for future generations. Every organization should have its own green campus and environment policy with respect to nature conservation and environmental protection and should maintain a sizable amount of green cover area after building construction along with natural and planted vegetation. A maximum number of more oxygen-producing and carbon-di-oxide-absorbing plants should be maintained to provide a pure atmosphere to the stakeholders. The installation of a rainwater harvesting system, percolation, ponds, check dam, and drip irrigation system to conserve rainwater and groundwater should be noteworthy on the campus

Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country’s capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration