Fathima Michael College of Engineering and Technology

This paper contains a research on recognizing and summarizing emotions from audio. This came about by integrating approaches involving noise removal to enhance signal clarity with further speech signal processing and Voice Activity Detection for extracting the required speech segments. The use of Automatic Speech Recognition facilitates the transcription of the audio content into text format, after which standard text preprocessing is carried out including lowercasing and removal of stop words. Thereafter, cues are extracted from the data using machine learning and tokenization. Summaries of emotional states are generated. Not only this approach enables efficient recognition of emotion in audio inputs, it also generates summaries that would bridge understanding across a huge variety of applications, ranging from human–computer interaction to sentiment analysis in customer feedback.

The growing dependence on technology in healthcare has resulted in the creation of sophisticated hospital networks that are highly linked and vulnerable to cyber threats. A reliable Network Intrusion Detection System (NIDS) is required to identify and prevent such cyberattacks. The network intrusion detection is vital for safeguarding hospital networks and guaranteeing data security. The CICIDS2017 dataset contains a comprehensive set of network traffic characteristics for assessing network intrusion detection systems. Besides that, class imbalance is a prevalent difficulty in intrusion detection and it may have a considerable impact on the effectiveness of classification algorithms. The suggested solution employs a Machine Learning (ML) based NIDS for hospital networks that utilizes CopulaGAN (Generative Adversarial Network) to address the challenges due to imbalanced class ratio. The synthetic samples of minority classes were created to balance the dataset and improve detection accuracy. The Random Forest (RF) algorithm is used to discover the most defining features in the dataset and its hyperparameters are tuned to improve classification performance. Overall, the CopulaGAN boosted Random Forest based NIDS described here is a valuable solution for detecting network intrusions in hospital networks.

The healthcare sector is rapidly evolving due to the exponential growth of the digital space and emerging technologies. Maintaining and effectively handling large quantities of data has become difficult in all industries. Furthermore, collecting helpful knowledge from extensive data collection is a daunting challenge. There would be an immense amount of data that continues to grow, making it harder and harder to find some helpful information. In the healthcare industry, big data analytics offers a variety of tools and strategies for detecting or predicting illnesses faster and delivering better healthcare facilities to the right patient at the right time to increase the quality of life. It is not as simple as one would imagine, given the myriad functional challenges that need to be addressed within current health data analytics systems that offer procedural frameworks for data collection, aggregation, processing, review, simulation, and interpretation. This chapter aims to design a long-term, commercially viable, and intelligent diabetes diagnosis approach with tailored care. Due to a lack of systematic studies in the previous literature, this chapter describes the different computational methods used in big data analytical techniques and the various phases and modules that transform the healthcare economy from data collection to knowledge distribution. The investigation findings indicate that the suggested framework will effectively offer adapted evaluation and care advice to patients, emphasizing a knowledge exchange approach and adapted data processing model for the smart diabetic system.

Big Data Analytics (B.D.A.) is a fast-growing field with the capability to provide useful, clear, and in-depth understanding solutions to healthcare applications. There are many dimensions of big data having issues with its usage, such as managing volume, velocity, variety veracity and value, characteristics such as integrity accuracy and interpretation. However, such challenges do not restrain us from using and exploring big data as a source of evidence in clinical application. This obtains the need to examine health care information to control and reduce the increased cost of medical treatment and improve patient's treatment outcomes. The goal is to describe the ability and capability of big data analytics in health care. This paper describes a review on the developing field of big data analytics in healthcare, discusses the advantages, and provides an architectural framework and methodology. It also investigates the data sources and suggests the platforms/tools for analytics, techniques, technologies, and also presents the challenges and conclusions. This paper concludes that Big data analytics in health care is a promising field for providing more in-depth insight from the massive volume of data sets and functional outcomes.

The molecular structure and molecular forces in liquids and solution in particular have been investigated by dielectric relaxation studies. The nature and strength of the molecular interactions have been established as the main cause for the chemical behavior of compounds. The dielectric behavior of dioctyl phthalate and diethyl phthalate with isobutanol has been studied at microwave frequency 9.36 GHz at different temperatures 303K, 313K and 323K. Different dielectric quantities like dielectric constant (e’), dielectric loss (e’’), static dielectric constant (e 0 ) and dielectric constant at optical frequency (e ∞ ) have been determined. The relaxation time (τ) has been calculated by both Higasi’s method and Cole-Cole method. The complex system investigated shows the maximum relaxation time values at temperatures by both Higasi’s method and Cole-Cole method.