Volume 11 | Issue 4 |

 Abstract

After the COVID-19 virus was introduced to the world, the WHO declared a global pandemic. It affected over 150 countries. To minimize the spread of the virus, schools, offices, and markets were temporarily closed. During the pandemic, many of the lecture sessions were conducted on-line, which caused a surprise for the teaching fraternity and the stack holders. It is with this background the researchers have made an attempt to study the willingness of the Faculty members to teach online Post-pandemic with special emphasis on Faculty in Private engineering colleges in Bangalore. The study was conducted among 144 Faculty members using a mixed-method approach to collect and analyze qualitative and quantitative data. The various phases of the study included: literature search, review, problem formulation, data collection, and analysis. Using SPSS .24 software the collected data was analyzed and the statistical tools applied were Cross Tabulation, Chi-Square tests, Correlation Method and Garrett Ranking Method. Majority of the Teaching Faculty contacted for this survey expressed their readiness to teach online Post-pandemic. This research also highlights two important issues that Teaching Faculty face: Lack of Equipment and Erratic/Unstable Internet Connection. Hence, the respective managements should pay attention to these issues on a priority basis to provide quality teaching to the teaching community.

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 Keywords

COVID-19, pandemic, teaching fraternity, stack holders, Post-pandemic

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Creative Commons Attribution 4.0 and The Open Definition

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Women are the pillars of society. Though their contribution to the social, political and economical plethora is not given priorities, their contribution to the household activities can't be denied by anyone. Due to the existing gender roles in the society, women are always expected to be within the four walls of their own homes. Perhaps, this results in more inclination of women towards the environment. Because women rely on the environment for their household chores- starting from cooking to relaxing. Women are the best collectors of natural resources. They depend on natural resources such as water, trees, land etc for their household chores. Hence, the loss of the environment directly impacts the lives of women. Forest is considered one of the most important natural resources and it becomes mandatory to preserve and conserve it. It is important for maintaining the ecology and women play a very vital role in its protection. The destruction of the environment mostly affects women as they are the most attached to the environment. Women are the active agents of conservation and restoration of the environment . Globally researchers have been trying their level best to link gender equality with a balanced environment and to portray how degrading environmental situations can result in the enhancement of men- women gap as well as refusal to women's rights. For example, countries with more women in their parliaments are likely to set aside protected land and ratify environmental treaties. The main reason behind examining the issue of women and environment is to show the role of women in the development of a balanced sustainable environment. For example, in India women are seen to be attached to a forest.environment sometimes for household chores and sometimes due to their mother's kind of love. This paper tries to show the relationship between women and the environment, various environmental movements etc.

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 Keywords

Environment, Women, Environment conservation, Environment restoration

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores the use of k-means clustering as a powerful technique for customer segmentation in marketing. The strengths and limitations of k-means clustering are discussed, as well as the factors that affect the accuracy and effectiveness of segmentation using this technique. The paper also highlights the importance of data preprocessing, feature selection, and clustering evaluation in achieving robust and insightful customer segmentation. In conclusion, the paper provides recommendations for future research directions and practical implications for businesses seeking to use k-means clustering for customer segmentation.

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 Keywords

Customer Segmentation, K-means Clustering Algorithm, Marketing Strategy, Data Mining, Machine Learning, Customer Behaviour, Consumer Profiling, Customer Analytics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract All human life events are Profession. When man began to use knowledge to increase his power, he became an industrial scientist. The Sangam period was a period of great influence in industrial development. Neythal and Marutham during the Sangam period, the productivity of the land shows his industrial prowess. It is because of this that there are reports of professional skills in the literature. Thiruvalluvar Human Society describes the skills that development can achieve. This article examines professional skills among them.

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 Keywords

???????? (Hard Skill), ????????? (Soft Skill), ??????????? ???????? (Professional Skills), ?????????? ?????, (Management Skills), ???? (Virtue).

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This research suggests a visibility detection approach based on the identification of the taillight signals of the previous vehicle using vehicle pictures. We first construct the system for picture enhancement before identifying the vehicle from the enhanced image. We are building a system that primarily serves as a means of vehicle identification without using the taillights. The other is to recognize moving vehicles using taillight segmentation. Utilising a YOLOv2 detector network, automobiles are detected in this work. The enormous variety in picture appearance and imaging interference mean that even though numerous automated detection approaches, including those based on image processing techniques, have been developed, the detection performance can still be improved.

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 Keywords

: Atmospheric haze removal, Image Processing, YOLOV2, MATLAB

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Creative Commons Attribution 4.0 and The Open Definition

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Background: The digestive system is impacted by gastrointestinal tract (GI) illnesses, and conventional diagnosis calls for invasive treatments. Large datasets can be used to train deep learning models to accurately predict and diagnose GI tract diseases, allowing for early detection, fewer invasive procedures, and better patient outcomes. Methods: We gather and preprocess a sizable dataset to build a deep learning model for predicting gastrointestinal tract illness. Meaningful data is extracted using feature extraction techniques, and an appropriate deep learning model is chosen and trained on the preprocessed data. Lastly, performance measures are used to assess the trained model on a validation dataset. Findings: High accuracy rates have been reached for disorders including colon cancer, inflammatory bowel disease, and Crohn's disease, demonstrating the potential of deep learning models for the prognosis of gastrointestinal tract diseases. Improved patient outcomes and lower healthcare costs can result from the adoption of non-invasive techniques for early detection and diagnosis. The models must be validated in clinical situations through more study. Novelty and applications: A innovative and non-invasive method of diagnostics is the application of deep learning models for the prognosis of gastrointestinal tract disorders. The use of this technology has the potential to completely transform the area of gastroenterology by enabling early identification and individualised treatment strategies. It can also lower medical expenses and enhance patient outcomes.

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 Keywords

Gastrointestinaltract,Data set,Crohn's disease,Detection,Gastroenterology.

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Creative Commons Attribution 4.0 and The Open Definition

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In this Project, a tentative design of a cloud-based Stroke prediction system had been proposed to detect impending Stroke using Machine learning techniques. For the accurate detection of the Stroke, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of Stroke detection. An accuracy level of 97.53% accuracy was found from the ML algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the Stroke patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, Blood flow, Heartbeat, Oxygen level. With the help of different decision-making algorithms decisions can be made easily and fast and according to its people can have access to the database. The main advantage of our system is it automatically generate the require prescription based on the vital parameters of the human.

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 Keywords

Machine learning techniques, learning algorithms and Nursing workflow

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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knowledge, Planned teaching program, Menopausal Osteoporosis, Perimenopausal women

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Creative Commons Attribution 4.0 and The Open Definition

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Digital agriculture involves the application of digital technology to integrate agricultural output from the field to the consumer. Digital advancements can help developing countries overcome global poverty and hunger in rural areas more quickly. Digitalization is commonly considered as having the potential to increase agricultural output and sustainability. However, the digitalization of agricultural innovation systems is likely to have broader ramifications. Agricultural knowledge and advice networks are critical components of agricultural innovation systems that are vulnerable to digital disruption. Major technological breakthroughs include indoor vertical farming, automation and robotics, livestock technology, current greenhouse practices, precision agriculture, artificial intelligence, and block chain. Farm equipment is linked to software platforms that collect on-farm data and enable studies of soil and climate conditions in specific locations to provide farmers with advice on seed selection and more precise pesticide and fertilizer application. The abundance of knowledge about plants and animals makes farming more visible to customers. Smart farming will have an impact on agricultural production in the long run. Mobile phones have had one of the highest rates of adoption of any technology developed in the last century. Consumers and farmers will become more connected as a result of digitalization. In this study, we examine developments in agricultural knowledge get prepared to deal with potential changes in these networks and focuses at trends in agricultural information and advisory systems both internationally and in India.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital agriculture, Mobile phones vertical farming, Digitalisation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cellulase enzymes are a group of complex proteins produced by microorganisms that play a critical role in breaking down cellulose, a major component of plant cell walls, into simple sugars. The three main types of cellulase enzymes are exoglucanases, endoglucanases, and ?-glucosidases, which work together in a sequence of interactions to efficiently break down cellulose into glucose molecules. This process is crucial for the recycling of cellulose in the natural environment and has great potential for industrial applications. One of the most promising industrial applications of cellulase enzymes is in the production of biofuels. Lignocellulosic materials, such as agricultural waste and forestry residues, can be used as a raw material for the production of biofuels. However, these materials are difficult to break down and require costly pre-treatment techniques to be converted into biofuels. Cellulase enzymes offer a potential low-cost alternative to these pre-treatment methods. Studies have shown that using cellulase enzymes to hydrolyze lignocellulosic materials can increase the yield and efficiency of biofuel production. Another promising industrial application of cellulase enzymes is in the paper and pulp industry. Cellulase enzymes can be used to improve the quality of pulp, increase paper brightness, and reduce the use of harmful chemicals in the production process. Additionally, cellulase enzymes have shown promise in textile cleaning and finishing, as they can be used to remove stains and improve the softness and color of fabrics. Recent advancements in biotechnology have led to the development of genetically modified microorganisms that can produce cellulase enzymes with improved properties and higher yields. Additionally, immobilization techniques have been developed to increase the stability and reusability of cellulase enzymes in industrial applications

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 Keywords

cellulase, exoglucanases, endoglucanases, ?-glucosidases, lignocellulase

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Creative Commons Attribution 4.0 and The Open Definition