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The Implications of IoT in the Modern Healthcare Industry post COVID-19
Full text linkThe healthcare industry has recently seen a massive surge in the use of the Internet of Things (IoT) during and after the COVID-19 pandemic. IoT’s main objective is to provide people with the necessities in these uncertain times. During the pandemic, the availability of IoT-based healthcare systems is crucial. Using IoT, healthcare systems are becoming more individualized, allowing for more precise patient diagnosis, treatment, and monitoring. Since the beginning of the epidemic, many researchers have worked tirelessly to find solutions to this global problem, and IoT technology has the potential to revamp the current system completely. Over 6 million people had lost their lives by the time this document was produced due to the ongoing COVID-19 epidemic. Many lives could have been saved. The problem today is that when people are too sick, they cannot call or contact an ambulance or get safely to the hospital. With new technology, perhaps a button or programming into a device, people in need can press a button on their phone or call out into a voice-enabled device to contact the ambulance or other emergency contacts that they might have. The research has found that if significant companies take this seriously, it could be a remarkable idea that could save many lives
Dr. Ruma Basu Gomes
No full textDr. Ruma Basu Gomes is presently working as an HR consultant, POSH adviser, soft skills trainer, coach and mentor. POSH policy maker and External member for All India Football Federation. Certified trainer from Director General of Shipping Govt. of India. HR Consultant and coach for many hazardous Industries. Having knowledge in psychotherapy, counselling and human relations. She has gone through NLP Business practitioner programme from onefluencer, Chennai. Trained in psychometric testing from Psytech International (UK) CBI,15FQ+, EQ, JTI,FIRO- B. EUM I (Mentoring Skills) certified from Sumedhas TA certified. IMS trained. Certified Lead auditor for Environment health and safety & Mental health in safety (14000, 45001 & 45003). She has passed certification program on (POSH) sexual harassment and workplace diversity from National University of Juridical Science (Kolkata) and I pleaders (Delhi).https://www.interscience.in/mentors/1101/thumbnail.jp
Predicting Accurate Heart Attacks Using Logistic Regression
Full text linkA heart attack is one of the leading causes of death today. According to a large data population used as a training set for the algorithm for machine learning, classification is a technique for predicting the target class from input data. A difficulty in clinical data analytics is predicting heart attacks with greater precision. The focal point of this work is to analyze the heart attack dataset (Kaggle repository) to find a Machine learning classifier technique that predicts if a person is prone to a heart attack with maximum accuracy based on various health factors. The efficacy of the three classifiers, namely Logistic Regression, Random Forest, and Decision Tree, is demonstrated for predicting heart attack. This work compares the three classification algorithms among various factors. Logistic Regression outperforms all for predicting the values from the dataset accurately
Data Analysis of Traditional Chinese Medicine Disease Diagnosis from the Perspective of Computational Sociology.
Full text linkIn Traditional Chinese Medicine (TCM), the diagnosis and treatment of diseases typically involve viewing the patient as a system and considering both the intrinsic natural mechanisms of the disease and the external sociological factors. However, a comprehensive and scientific standard for understanding the external sociological factors in TCM diagnosis and treatment has not yet been established. The main reason for this is the complexity of computing these sociological factors due to their openness, multidimensionality, and heterogeneity. Drawing insights from computational sociology, this study explores the latent sociological factors in TCM disease diagnosis and treatment. It aims to obtain sociological factor data related to diseases from various online sources, such as internet-based medical consultation platforms and social networks. Through data analysis, it seeks to reveal the correlations between diseases and sociological latent factors. The ultimate goal is to establish a pre-diagnosis sociological factor database for TCM diseases. This endeavor serves as a foundation for developing a more scientific online TCM disease consultation system, providing references for TCM disease diagnosis and treatment, and offering evidence-based health behavioral recommendations for disease prevention
How Modern Agronomy is Changing with AI and IoT post COVID-19 Pandemic: A Qualitative Study
Full text linkAI and IoT are changing our day-to-day lives in every aspect, including but not limited to banking, retail, and agriculture. Dreadful obstacles confronted by agrarian and food associations involve ecological deprivation and biodiversity deficit, enduring hardship, an increasing overweightness epidemic, nutrition uncertainty, and the use of ergonomics. However, managers and decision-makers frequently let down to admit how awful these matters are. Wicked difficulties want united act from the social order assemblies with profoundly apprehended, contrasting opinions and principles because their connectedness associations are stiff or incredible to detect, they cannot be expressed or resolved, deprived of detonating arguments amongst investors, and they cannot be resolved unaccompanied. All industries are directly or indirectly getting influenced by this modern technology. This research study aims to study the practical function of these technologies in the agri-food industry, which will be helpful in process models, stakeholder predictions, and correct environmental awareness to change the agri-business model. This research study findings highlight exciting issues and questions related to using AI in the agri-food industry towards the space economy to achieve a sustainable business model and better use of resources during the pandemic. Both hypothetical and administrative consequences are reviewed here
Dr. Yogesh Kumar Jain
No full textDr. Yogesh Kumar Jain is an Associate Dean-Academics, Professor & Program Chair Finance & Banking & Financial Services in the School of Commerce and Management Studies, Sandip University, Nashik, India. He received his Ph.D. degree in the subject of Business Administration from Mohan Lal Sukhadia State public University. He has completed Master of Business Administration from SGB Amravati University, Master of Finance from Jaipur National University, Master of Banking & Economics from MLS University, Master of Commerce (Business Administration) from MLS University. His area of teaching and research is Finance and Accounts. Dr. Jain He has worked at different levels with many reputed Universities like Sikkim Manipal University, RNB Global University, & Presidency University. He guided many scholars who got awarded Doctorate and he is having a rich research experience in designing &implementing new courses, postgraduate and undergraduate degree programs & certificate programs. Dr.Jain has contributed in higher education with many academic Research & publications published in various reputed National & International Journal. Dr.Jain is also actively involved in books, book chapters writing, and academic delivery for Finance & Accounts related subjects. Dr.Jain has also received many reputed awards along with recognition as a best teacher from different reputed organizationshttps://www.interscience.in/mentors/1104/thumbnail.jp
Trade Openness in China’s Economy: A Review Study
Full text linkTrade openness has been established and documented to foster economic growth and development. However, this is coupled with problems. In this light, this paper employs a qualitative approach to review the relationship between trade openness and Foreign Direct Investment (FDI) inflow, Economic growth and carbon dioxide (CO2) emissions using China as the case study. The study revealed that trade openness has its benefits as well as its problems. That is, trade openness significantly influences economic growth, FDI, and the emission of CO2 in the atmosphere in China. Consequently, CO2, FDI, and economic growth are significantly influenced by China\u27s level of trade openness. For instance, in China, trade openness augments economic growth and influences foreign direct investment inflow positively. However, it increases the amount of carbon dioxide in the atmosphere. It is therefore important for policymakers to carefully consider these relationships when designing policies aimed at reducing carbon emissions and promoting sustainable economic growth
National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)
Full text linkAs we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp
Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms
Full text linkIn response to the challenges of learning confusion and information overload in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data scarcity issues of existing traditional recommendation algorithms. Analyze the characteristics of the Knowledge graph of learners and curriculum resources in depth, use the graph Auto encoder to extract the auxiliary information and features in the Knowledge graph and establish the corresponding feature matrix, and use Convolutional neural network for classification and prediction. The experimental results show that this algorithm improves the performance of recommendation systems, improves learners\u27 learning efficiency, and promotes personalized development
Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU
Full text linkA time sequence analysis is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance