A Study on the Higher Education System in India and Factors Affecting the Choice of Teaching Career in IT Education

India is a country with high educational values and is considered as a country with an asset of technically sound, motivated and hardworking student strength and dedicated faculty members. But at the same time, there exists shortage of such talented faculty members across different Universities and affiliated Institutes in India. The research objective of the paper is to analyse the different factors that guide teaching career decisions among IT engineering graduates and post-graduates and to find the most significant factors which influences such career choices. The statistical analysis presented in the paper is based on a Survey conducted on the Graduate and Post-Graduate students in a private University in India. The factors considered in the study are Motivation, Perception, Interest, Professional and Financial Security, Career Choice based on Knowledge and skill and finally Inclination towards research. The study reveals the effect of Gender on the perception of teaching career choice and also shows the existence of significantly different effect of the Semester/year of study on the different factors that affects teaching career. The study also reviews the present higher education scenario in India and correlates the factors that affect faculty shortage and career choice based on present Indian perspective. Based on the statistical analysis results the paper provides list of recommendations which would be beneficial and would encourage more individuals to choose a teaching career. DOI: 10.5901/mjss.2015.v6n4s1p6

A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions

Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial intelligence (AI), opens opportunities to deal with privacy issues in healthcare systems and exploit data and computing resources available at distributed devices. Additionally, the Metaverse, through integrating emerging technologies, such as AI, cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, has transformed many vertical domains in general and the healthcare sector in particular. Obviously, FL shows many benefits and provides new opportunities for conventional and Metaverse healthcare, motivating us to provide a survey on the usage of FL for Metaverse healthcare systems. First, we present preliminaries to IoT-based healthcare systems, FL in conventional healthcare, and Metaverse healthcare. The benefits of FL in Metaverse healthcare are then discussed, from improved privacy and scalability, better interoperability, better data management, and extra security to automation and low-latency healthcare services. Subsequently, we discuss several applications pertaining to FL-enabled Metaverse healthcare, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight significant challenges and potential solutions toward the realization of FL in Metaverse healthcare.Comment: Submitted to peer revie

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Stroke is enlisted as one of the leading causes of death and serious disability affecting millions of human lives across the world with high possibilities of becoming an epidemic in the next few decades. Timely detection and prompt decision making pertinent to this disease, plays a major role which can reduce chances of brain death, paralysis and other resultant outcomes. Machine learning algorithms have been a popular choice for the diagnosis, analysis and predication of this disease but there exists issues related to data quality as they are collected cross-institutional resources. The present study focuses on improving the quality of stroke data implementing a rigorous pre-processing technique. The present study uses a multimodal stroke dataset available in the publicly available Kaggle repository. The missing values in this dataset are replaced with attribute means and LabelEncoder technique is applied to achieve homogeneity. However, the dataset considered was observed to be imbalanced which reflect that the results may not represent the actual accuracy and would be biased. In order to overcome this imbalance, resampling technique was used. In case of oversampling, some data points in the minority class are replicated to increase the cardinality value and rebalance the dataset. transformed and oversampled data is further normalized using Standardscalar technique. Antlion optimization (ALO) algorithm is implemented on the deep neural network (DNN) model to select optimal hyperparameters in minimal time consumption. The proposed model consumed only 38.13% of the training time which was also a positive aspect. The experimental results proved the superiority of proposed model

Diabetic Retinopathy Detection: A Blockchain and African Vulture Optimization Algorithm-Based Deep Learning Framework

Blockchain technology has gained immense momentum in the present era of information and digitalization and is likely to gain extreme popularity among the next generation, with diversified applications that spread far beyond cryptocurrencies and bitcoin. The application of blockchain technology is prominently observed in various spheres of social life, such as government administration, industries, healthcare, finance, and various other domains. In healthcare, the role of blockchain technology can be visualized in data-sharing, allowing users to choose specific data and control data access based on user type, which are extremely important for the maintenance of Electronic Health Records (EHRs). Machine learning and blockchain are two distinct technical fields: machine learning deals with data analysis and prediction, whereas blockchain emphasizes maintaining data security. The amalgamation of these two concepts can achieve prediction results from authentic datasets without compromising integrity. Such predictions have the additional advantage of enhanced trust in comparison to the application of machine learning algorithms alone. In this paper, we focused on data pertinent to diabetic retinopathy disease and its prediction. Diabetic retinopathy is a chronic disease caused by diabetes and leads to complete blindness. The disease requires early diagnosis to reduce the chances of vision loss. The dataset used is a publicly available dataset collected from the IEEE data port. The data were pre-processed using the median filtering technique and lesion segmentation was performed on the image data. These data were further subjected to the Taylor African Vulture Optimization (AVO) algorithm for hyper-parameter tuning, and then the most significant features were fed into the SqueezeNet classifier, which predicted the occurrence of diabetic retinopathy (DR) disease. The final output was saved in the blockchain architecture, which was accessed by the EHR manager, ensuring authorized access to the prediction results and related patient information. The results of the classifier were compared with those of earlier research, which demonstrated that the proposed model is superior to other models when measured by the following metrics: accuracy (94.2%), sensitivity (94.8%), and specificity (93.4%)