M. G. Prasad

This chapter explores challenges associated with the walkability of a destination through a case study and from the perspective of women living in Bodh Gaya, India, who hail from more walking-friendly places. Bodh Gaya is a well-known Buddhist tourism destination that sees an abundance of pilgrims from across the globe. Apart from being home to the “Mahabodhi” temple, a world heritage site, the Nagar Panchayat boasts newer temples and monasteries funded by different countries, each of which contribute a distinct cultural experience, offering a multiverse of attractions for tourists. Tourism-related businesses form the backbone of the economy of Bodh Gaya. For such a renowned heritage tourism destination, walkability is relevant from various perspectives. However, the walkability of the destination remains limited, with attempts to improve it only introduced in 2018. We investigate this attempt to improve walkability by examining the experience of female university professors who joined the local Magadh University in 2018 and live in Bodh Gaya. We also use secondary data from newspapers that captures the point of view of stakeholders on improvements. We present focus group findings followed by qualitative interviews in light of interactions between Sustainable Development Goals 5 (gender equality), 8 (decent work and economic growth), 11 (sustainable cities and communities), and 12 (responsible consumption and production). We provide suggestions for policymakers in such urban planning situations and ideas to manage host population hostility toward visitors.

This paper proposes a neural-network-based detection of common cold disease through speech signal processing. Speech is formed by the movements of vocal cords present in the vocal tract. The vocal tract includes organs like the nasal cavity, the oral cavity, the upper throat (pharynx), and larynx. A common cold is a viral infection that affects the upper respiratory tract, which includes nose, throat, sinuses, and airways. Therefore, a common cold alters a person’s vocal ability. The goal of the proposed study is to create two classification models based on long short-term memory (LSTM) and gated recurrent unit (GRU) that can properly determine from an individual’s voice whether they are infected with common cold. Through the use of these architectures we highlight the role of speech signal processing in the early detection and diagnosis of the common cold. The results show satisfactory accuracy in detecting cold speech. The results are preliminary, and by balancing the dataset and focussing on a wider sample of sick individuals, it may be possible to improve accuracy.

In this investigation, two new features, residual harmonic amplitude (RHA) and residual harmonic frequency (RHF) are proposed using Fourier model-based analysis of the linear prediction (LP) residual signal for diagnosing Parkinson’s disease (PD) from the speech signal. The excitation source information is represented by the LP residual of the speech signal. The LP analysis determines the LP residual of speech by performing inverse filtering operations on the speech signal. The proposed RHA and RHF features are extracted from the LP residual using Fourier model analysis. The probability density function (PDF) plots and box plots are used to examine the importance of the proposed features in classifying PD patients and healthy people. The effectiveness of the proposed features for detecting PD is evaluated using the support vector machine (SVM) classifier. The results show that the proposed RHA and RHF features are effective for detecting PD. The proposed features give comparable performance with linear predictive coefficient (LPC) features for discriminating between PD-affected and healthy speech.

Over the last several years, the fields of affective computing and human–computer interaction have shown a great deal of interest in the topic of speech emotion recognition studies. In the study of speech emotion classification, numerous strategies, many of which are well-established methodologies, have been utilized to extract emotions from speech signals. These strategies include speech analysis and classification methods. Deep learning techniques have recently been proposed as a potential alternative to more traditional methods for the classification of the emotions conveyed in speech. In this study, we used MFCC features for emotion classification and compared the performance of DNN, DCNN, and CNN-LSTM models on RAVDESS, EMODB, and TESS datasets. In the proposed models, DNN achieved an average accuracy of 76.38% on RAVDESS, 82.83% on EMODB, and 100% on TESS. The overall accuracy of DCNN was 74.65% when it was tested on RAVDESS, 77% when it was tested on EMODB, and 100% when it was tested on TESS. On RAVDESS, CNN-LSTM achieved an average accuracy of 70.27%, while on EMODB it achieved 71.8%, and on TESS it achieved 99%.

Diversity of bird species in Ado odo wetland Southwest Nigeria was examined in this study. Birds of the same species within 10m of each other were counted in the same group. Human threats to the study area were also examined. Forty-two wetlands within the Ado – Odo Wetlands complex were surveyed. Data obtained from the field survey were entered into excel spread sheet prior to both descriptive and analytical statistics. The computer PAST Model version 3 was used to analyze bird species diversity indices, SHE analysis, and plot generalized linear model graph. A total number of 120 bird spices belonging 39 families and 15 orders were enumerated in the study area.The result indicates that Ardeidae has the highest number of bird species, this is followed by Ploceidae with 9 bird species. The result of the relative abundance of bird species in the study area indicates that it was higher in the dry season than the wet season 0.0013). The Shannon diversity index showed that it was higher during the dry season than the wet season. Bird species diversitywere higher in lakes and ponds than marshyland The status of the bird species in the study area indicates that resident bird species were highest, followed by Intra Africa Migrants and Palearctic migrants. The total number of bird species recorded during the dry season was while the wet season is

We study the different solution strategy for solving epidemic model in different imprecise environment, that is, a Susceptible-Infected-Susceptible model in imprecise environment. The imprecise parameter is also taken as fuzzy and interval environment. Three different solution procedures for solving governing fuzzy differential equation, that is, fuzzy differential inclusion method, extension principle method, and fuzzy derivative approaches, are considered. The interval differential equation is also solved. The numerical results are discussed for all approaches in different imprecise environment.