Volume 12 | Issue 5 |

 Abstract

The exponential growth of electronic waste (e-waste) presents a critical environmental and public health concern globally, necessitating effective management strategies. This study investigates household practices in e-waste management in Tambaram, Tamil Nadu, India, employing a mixed-method approach involving primary data collection through questionnaires and secondary data analysis. The research aims to evaluate awareness levels and current practices among Tambaram residents regarding responsible disposal methods and to propose sustainable strategies tailored to community needs. Findings reveal a correlation between household income and electronic purchasing behavior, with a significant segment possessing considerable purchasing power. Despite the prevalence of unused electronic items in households, proactive measures such as promoting recycling programs and raising awareness are imperative. Most households tend to retain unused electronics at home or dispose of them with regular waste, emphasizing the need for educational initiatives and convenient disposal options. Concerns about the environmental impact of e-waste disposal are prevalent, yet awareness of recycling programs remains inadequate. However, a majority express willingness to modify disposal habits to mitigate environmental impacts, suggesting a potential shift towards sustainable practices. The study underscores the urgent need for enhanced education, infrastructure development, and community engagement to address the challenges associated with e-waste accumulation effectively. By offering insights and recommendations, this research contributes to the development of targeted interventions for responsible e-waste disposal and recycling in urban and semi-urban areas.

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 Keywords

: E-waste management, Household practices, Awareness, Recycling, Sustainability

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Creative Commons Attribution 4.0 and The Open Definition

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In today's knowledge-based economy, investors and stakeholders place significant value on IP assets when evaluating startup opportunities. A robust IP portfolio not only demonstrates the uniqueness and market potential of a startup's offerings but also provides assurance to investors regarding the startup's ability to protect its innovations, establish market presence, and generate sustainable revenue streams . This paper aims to provide startups with comprehensive insights, strategies, and best practices for effectively protecting their IP assets to enhance their chances of securing funding. By understanding the nuances of IP protection, leveraging IP for competitive advantage, navigating IP challenges in fundraising and building a winning IP protection strategy, startups can position themselves strategically in the investment landscape and unlock opportunities for growth, innovation, and market leadership.

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Intangible assets, including patents, trademarks, copyrights

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Creative Commons Attribution 4.0 and The Open Definition

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This project pioneers a novel approach to predicting cardiac disease using Machine Learning algorithms like LR, KNN, SVM, GBC, and the powerful Extreme Gradient Boosting Classifier (XGBoost) with GridSearchCV. Utilizing 5-fold cross-validation, it assesses performance across diverse datasets. The XGBoost Classifier with GridSearchCV achieves outstanding accuracy, hitting 100% in testing and 99.03% in training across multiple datasets, outperforming other algorithms and previous studies. Notably, the XGBoost Classifier without GridSearchCV also demonstrates strong accuracy. Furthermore, an extension employing Random Forest shows comparable accuracy with reduced computation time. This research underscores the efficacy of the proposed technique in advancing cardiac disease prediction.

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 Keywords

ML, cardiac disease

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Creative Commons Attribution 4.0 and The Open Definition

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Peripartum cardiomyopathy (PPCM) poses a significant clinical challenge, presenting as heart failure in women during late pregnancy or postpartum. Despite medical advancements, PPCM diagnosis remains elusive due to its variable symptoms and absence of definitive criteria. This review comprehensively explores PPCM, spanning epidemiology, pathophysiology, clinical manifestations, diagnostic approaches, management strategies, and prognostic considerations. Enhanced understanding of PPCM complexities equips healthcare providers with the tools necessary for early identification, timely intervention, and improved outcomes, thereby underscoring the critical need for heightened awareness and multidisciplinary collaboration in managing this potentially life-threatening condition.

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Peripartum, Prolactin, Cardiomyopathy,myocardial dysfunction.

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The purpose of the study was to create and assess a dental gel with clove oil as its main ingredient for the treatment of periodontitis. Clove oil is chosen for the treatment of periodontitis because of its broad range of antibacterial activity against several periodontal infections. Using carbopol 934 as a gelling agent, clove oil as a therapeutic agent, polyethylene glycol as a co-solvent, methyl and propyl parabens as preservatives, and the necessary amount of distilled water as a carrier, clove oil gel is created. Gingivitis and periodontitis are examples of inflammatory conditions that are generated by plaque and are usually referred to as periodontal disease. Gingivitis is a mild stage of disease that is characterized by swelling, minor bleeding, and redness of the gingival edge. It is brought on by an accumulation of supragingival plaque. A shift in the microbiota from a more Gram-negative to a more Gram-positive anaerobic flora is linked to gingivitis. The more advanced form of periodontal disease known as periodontitis causes the alveolar bone to resorb and the periodontal ligament that supports the tooth to separate. In order to create a dental gel that would help treat periodontal illnesses, clove oil was added. The gel's physicochemical characteristics, such as its drug content, spreadability, extrusion ability, and in vitro antibacterial activity, were then assessed.

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 Keywords

periodontitis, dental gel, clove oil, carbopol 934, polyethylene glycol, methyl paraben, propyl paraben, distilled water, gingivitis, inflammatory conditions, plaque, periodontal disease, supragingival plaque, microbiota, Gram-negative, Gram-positive, anaerobic flora.

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In the fight against brain cancer, accurate brain tumor detection is crucial for early diagnosis and effective treatment. This study explores various machine learning and deep learning techniques to achieve this goal using MRI scans. We investigated the effectiveness of several methods: Convolutional Neural Networks (CNNs) achieved an impressive test accuracy of 86.27%. This demonstrates their ability to learn important features directly from MRI images. Multilayer Perceptrons (MLPs) were explored in two ways. A standalone MLP trained on features extracted using Principal Component Analysis (PCA) reached an accuracy of 76.47%. We also experimented with using an MLP in a transfer learning approach with InceptionV3 for feature extraction. This approach yielded results to be discussed alongside the standalone models. We also compared the performance of several other machine learning techniques alongside the MLPs and CNNs. A standalone MLP, trained on its own without any transfer learning, achieved an accuracy of 52.94%. We also evaluated the VGG16 convolutional neural network, which reached an accuracy of 70.58%. Logistic regression, a common statistical method, yielded an accuracy of 62.74%. Random forest, an ensemble learning technique that combines multiple decision trees, achieved an accuracy of 72.54%. Ada boosting, another ensemble learning method, performed quite well, reaching the highest accuracy (74.50%) among all the non-deep learning models. For other machine learning models, Naive Bayes achieved an accuracy of 68.62%, while SVM (Support Vector Machine) reached 60.78%. Similarly, a decision tree model resulted in an accuracy of 68.62%. Bagging, another ensemble technique, yielded an accuracy of 66.66%. Interestingly, a hybrid model that combined the pre-trained VGG-16 and InceptionV3 models achieved an accuracy of 68.92%.The results reveal that Convolutional Neural Networks (CNNs) were the most successful method, achieving the highest accuracy (86.27%) for brain tumor detection in MRI scans. This suggests that CNNs are particularly adept at learning the critical patterns hidden within the MRI image data.

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Creative Commons Attribution 4.0 and The Open Definition

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The rise of non-communicable diseases (NCDs), such as heart disease and diabetes, underscores the crucial necessity for innovative predictive solutions. This study offers a framework for improving the detection, diagnosis, and management of cardiovascular diseases (CVDs) by machine learning algorithms. Recognizing the intricate relationship between modernization, commercialization, and unhealthy lifestyles, our research presents a novel application process designed specifically for medical professionals. With an emphasis on heart disease, our software helps physicians predict the onset or recurrence of non-communicable diseases (NCDs) by utilizing patient records. Thorough testing confirms the program's effectiveness and quick prediction skills, enabling well-informed choices on patient health risks. Our software prototype uses various risk characteristics to classify the risk of abrupt cardiac events in patients diagnosed with ischemic heart disease (IHD). This paper discusses different techniques that aim to raise awareness and facilitate preventive interventions to lower the occurrence of such events.

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Non-Communication Diseases (NCDs), Cardio vascular diseases(CVDs), Machine learning algorithms, Predictive solutions, Heart disease, Patient records, Risk analysis, Preventive interventions

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This pioneering project tackles the pressing issue of gas emission detection, crucial for environmental and human well-being. Conventional detection systems face limitations, prompting the exploration of hyperspectral image analysis for a safer and more efficient solution. Introducing a groundbreaking deep learning methodology for hyperspectral gas detection in the longwave infrared spectrum, this project merges unmixing and classification techniques. Through a specialized 3-D convolutional neural network and autoencoder-based network, it converts radiance data to luminance-temperature data, achieving remarkable performance surpassing conventional methods. Further innovation extends the approach with an Ensemble model, integrating CNN, Bi-directional, and GRU algorithms, enhancing input features for improved prediction accuracy. This innovative endeavor underscores the efficacy of modern techniques in addressing environmental challenges.

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Gas, 3D CNN

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Creative Commons Attribution 4.0 and The Open Definition

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Sensing is the main function of all autonomous driving and collects all the necessary information about the environment around the vehicle's movement. The decision making process uses the information needed to create strategies and make the right decision based on the situation to ensure the safety of passengers as much as possible. This article reviews recent literature on motor vehicle awareness (AVP), focusing on two main tasks: semantic segmentation and object detection. These two functions play an important role as an important part of the car navigation system. In the autonomous car, perception with point cloud semantic segmentation helps obtain a wealth of information about the surrounding road environment. Despite the massive progress of recent researches, the existing machine learning networks are still insufficient for online applications of autonomous driving due to too subdivided classes, the lack of training data, and their heavy computing load

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Local interpretation model-agnostic explanation,adaptive dehazing,metric learning

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Creative Commons Attribution 4.0 and The Open Definition

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The Mughal Empire, spanning from the 16th to the 19th centuries in the Indian subcontinent, witnessed the convergence of diverse culinary traditions, resulting in the rich and opulent Mughal cuisine. This abstract explores the history of food in the Mughal Empire, focusing on its culinary influences, agricultural practices, dining culture, and societal significance. Mughal cuisine, characterized by its fusion of Central Asian, Persian, and Indian culinary traditions, reflected the cosmopolitan nature of the empire. Agriculture played a crucial role in sustaining the empire's vast population, with sophisticated irrigation systems and crop cultivation techniques supporting the production of staple grains, fruits, and vegetables.

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Creative Commons Attribution 4.0 and The Open Definition

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Grains, cultivation techniques

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Creative Commons Attribution 4.0 and The Open Definition