Volume 11 | Issue 9 |

 Abstract

India is a country of rich traditional knowledge. The people of this land, through their indigenous knowledge, have been able to understand and use the complexities of life to find the best possible solutions over time. They had an innovative approach to addressing the challenges of different life forms. Dharamapala's works represent the knowledge system that prevailed in India before the European invasion. People who came from different places also started to spread the knowledge in their countries. It's a known fact that environment and ecology comprising air, earth, water, plants and animals play a very significant role imperative in sustaining human life and civilization. Human civilization can flourish only if there is consistent harmony among various stakeholders in making optimum sustainable use of the world natural resources and take responsibility to protect the environment from any degradation and damage. Indian presidency adopted an action-oriented approach and the G20 explored mechanisms that can support timely and adequate mobilization of resources for climate finance, facilitating access to multilateral climate funds and enhancing their leverage and ability to mobilize private capital has also been the focus.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sustainability, Inclusiveness, responsibility, ancient wisdom, cultural economy, global economy

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The adoption of renewable energy sources in modern India has significant implications for ecology, environment, and tourism. This paper provides a comprehensive overview of the potential benefits and challenges associated with renewable energy projects in India. We discussed the environmental impact of renewable energy sources, the social and economic impact, and the impact on tourism. We also explored several examples of renewable energy projects and their impact on different aspects of Indian society. In conclusion, we emphasized the importance of a sustainable and socially responsible approach to the adoption of renewable energy sources in India, and the need for policies and regulations that prioritize environmental protection and the rights of local communities.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ecology, Environment, and Tourism

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The field of biosciences have progressive to a higher extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease (CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worse by time is high. The very common problems that results due to a kidney failure are heart diseases, anemia, bone diseases, high potassium, and calcium. The worst-case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can increase the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage. Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing, data transformation and various classifiers to predict CKD and proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chronic Kidney Disease, Machine Learning Approaches, Kidney Disease Prediction.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kidney disease poses a significant health challenge, with kidney stones being a common manifestation that requires timely diagnosis and treatment. Traditional diagnostic methods often rely on radiological imaging, which can be time-consuming and subjective, heavily reliant on the expertise of radiologists. This research introduces a novel approach to automate the detection of kidney stones using deep learning techniques. In this study, a diverse dataset of medical images, encompassing normal kidney tissue, kidney stones, cysts, and tumors, is collected and annotated. Two state-of-the-art deep learning architectures, VGG16 and ResNet50, are employed for the classification task, leveraging their ability to extract intricate details from medical images. The methodology involves data preprocessing, including dataset balancing to prevent class imbalance bias. Feature selection techniques are applied to improve classification accuracy by removing irrelevant and redundant attributes. Convolutional Neural Networks (CNNs) are utilized, comprising convolution, pooling, and fully connected layers, to identify patterns in the images and classify them into the relevant categories. Results demonstrate the efficacy of the proposed deep learning models, achieving an impressive accuracy rate of 99.36% in the classification of kidney stones, cysts, tumors, and normal kidney tissue. Confusion matrix analysis further validates the model's accuracy. The outcomes of this research indicate that deep learning models can significantly expedite the diagnostic process for kidney disease, reduce healthcare costs, and potentially lead to earlier diagnoses and treatments. Moreover, the burden on radiologists can be alleviated through the deployment of a fully developed automated system for kidney illness detection.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Kidney disease poses a significant health challenge, with kidney stones being a common manifestation that requires timely diagnosis and treatment. Traditional diagnostic methods often rely on radiological imaging, which can be time-consuming and subjective, heavily reliant on the expertise of radiologists. This research introduces a novel approach to automate the detection of kidney stones using deep learning techniques. In this study, a diverse dataset of medical images, encompassing normal kid

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Sign Language Detection system is developed for detecting the alphabets of American Sign Language (ASL). Conventionally, sign languages comprise of finger spelling. For detecting the signs, the Regions of Interest (ROI) are related and tracked employing the Landmarks feature of Media Pipe. Then by using Open CV, we capture the landmarks of the hands, and the key points of landmarks are stored in a NumPy array. Then we can train the model on it by using TensorFlow, Keras, and CNN. Finally, the model can be tested in real-time by ingesting live feed incoming from the webcam. Realtime Sign Detection is one of the essential applications to be used by deaf and dumb people as it helps them to communicate with others effectively. Historically, various methodologies for detecting signs were employed by the Machine Learning Algorithm, by training it on the image data . However, now we are employing Deep Learning Models to simplify and speed up the process of real-time sign detection and recognition and produce equal or more accuracy using smaller amounts of data.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

American Sign Language, Realtime, Media Pipe, Landmarks, Key points, OpenCV, NumPy, CNN, TensorFlow, Keras.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sanskrit has been a vehicle of Indian traditional wisdom.The literary form known as "K?vyas", as it is appropriately termed, retains its creative vitality in both prose and poetry. Mah? K?vyas, Laghu K?vyas, or Khan?a K?vyas are two literary genres created by our Indian poets that are both prose and poetry masterpieces, rich in all facets of culture. To a significant extent, a new vision of progress has been escorted by the genre of A??apadi known as G?ta K?vyas. The poet Jayadeva's composition G?ta -Govinda, written in the twelfth century, is considered to be the foundational work of the A??apadis genre. This outstanding literary work paved the path for numerous other poets to replicate it and adapt it to their own works. This academic study investigates the diversity of the A??apadi G?ta -Govinda's lyrical genre and its emulations.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

G?ta K?vya, A??apadi, G?ta-Govinda, Jayadeva, Bhakti, Rasa, etc.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Forest tribals of different states of India earn their livelihood by getting economic support from minor forest produce. Various measures have been taken by the Central Government and the State Governments in collecting minor forest produce, marketing, selling it, etc., through which the standard of living of the tribals can be raised. Government's MSP for MFP and Van Dhan Yojana are very useful schemes for collection, marketing and sale of minor forest produce. Through it, many tribals of different states of the country have got financial assistance.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Tribal, Tribal Economy, MSP for MFP, Van Dhan Yojana, TRIFED

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

CORRECT CIRCUMFERENCE WILL FIND CORRECT DIAGONAL OF THE SQUARE - HONOURABLE RISHI SUNAK METOD (1732ND PROOF OF REDDY?)

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CORRECT CIRCUMFERENCE WILL FIND CORRECT DIAGONAL OF THE SQUARE - HONOURABLE RISHI SUNAK METOD (1732ND PROOF OF REDDY?)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Employee experience (EX) is the experience of employees right from the day of job candidacy until the day they exit the organization. It combines three distinct environments, namely technological, physical, and cultural, irrespective of the organization's type, size, and scope. According to a recent study, negative employee experience is one of the top reasons behind the high attrition rate of employees. To recruit and keep the greatest talent today, businesses must consider their employees' touchpoints, including their organization's culture, technology, physical environment, etc. Before accepting a job, salary may be a major concern, but with millennials; it's not the only case. They are more concerned about positivity at the workplace and their experience with the organization. Over the past few years, employee engagement gained popularity, and a lot of research has been done on its theoretical and practical perspective. However, very little research has been done on the necessity of employee experience for effective employee engagement and satisfaction. This paper highlights the Employee experience trends to improve workforce productivity. It has also emphasized the relevance of positive EX with the productivity of remote workers. The potential contribution of this study will provide useful insights on how to increase the productivity of the workforce through positive employee experience for various professionals, practitioners, and academicians in the field of HR management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

employee experience, remote workers, workforce productivity

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Wireless communication is made up of numerous constrained sensor node with constrained resources such as Energy capacity, Data transmission capacity, Battery longevity, Storage capability, Computational capacity. Sensor nodes generate massive volumes of data, which are subsequently gathered and transmitted to the sink through single or multi-bounds paths. Due to inadequate data transfer across nodes and excessive energy consumption caused by rising data transmission delay, the network becomes unstable due to the limited skills of the nodes. Network performance can be significantly impacted by sink locations and the paths that sensors take to get there. Although several solutions have been put out to deal with this problem, they frequently lead to networks that have limited lifespans, consume a lot of energy, and transport data slowly. Clustering is a helpful method for overcoming these constraints that can help wireless sensor networks operate better by consuming less energy. With the goal of increasing the lifespan of wireless sensor networks, this study applies a clustering technique to predict the optimization of two or more sink deployment and sensor-to-sink communication. We aggregate nodes using an improved mean shift approach initially, and then choose the cluster head using a QOS-aware cluster head selection. The suggested approach uses sleep scheduling and cluster-based routing for data transmission, which aids in quick routing and energy conservation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

wireless sensor networks; sink; mean shift algorithm; [QoS] Quality of Service; sensor-to-sink routing; data transmission; cluster based routing, sleep scheduling.

  License

Creative Commons Attribution 4.0 and The Open Definition