Study of an Innovative Approach to IoT Based Human Activity Recognition | Chapter 19 | Leading the Charge: A Guide to Management, Entrepreneurship and Technology in the Dynamic Business Landscape Edition 1

Recognizing human activities is vital for numerous contemporary applications rooted in the Internet of Things (IoT) framework, spanning from the creation of intelligent video surveillance setups to the advancement of robotic assistants for the elderly. Recently, there has been significant exploration into machine learning algorithms to enhance the recognition of human activities. Despite these research endeavors, there remains a notable dearth of studies focusing on efficiently recognizing complex human activities, particularly those involving transitions, and no research has been conducted to assess the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities, designed to optimize the classification of both transitional and non-transitional human activities. Utilizing unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely-used smartphones, the system effectively identifies human activities [1,2]. The key feature of the proposed system lies in leveraging a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the reliability and accuracy of the proposed system compared to state-of-the-art classifiers, highlighting its robustness in handling noisy data. Human Activity Recognition (HAR) is essential for various modern applications within the Internet of Things (IoT) framework, from developing intelligent video surveillance systems to enhancing robotic assistants for the elderly. Despite significant advancements in machine learning algorithms for HAR, there is a notable lack of research on effectively recognizing complex human activities, particularly those involving transitions, and assessing the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities. Designed to optimize the classification of both transitional and non-transitional human activities, the system employs unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely used smartphones [1,3]. A key feature of the proposed system is the utilization of a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the system's reliability and accuracy compared to state-of-the-art classifiers, emphasizing its robustness in handling noisy data.

 

Author (s) Details

 

Motashim Rasool
Integral University, India.

 

Rizwan Akhtar
Integral University, India.

 

Uvais Ahmad
Integral University, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-98-3/CH19

Effects of Xiaoyaosan on the Hippocampal Gene Expression Profile in Rats Subjected to Chronic Immobilization Stress | Chapter 7 | Insights into the Prevention and Treatment of Depression with Famous TCM Prescription Xiaoyaosan

Objective: This study examined the effect of Xiaoyaosan and its anti-stress mechanism in rats subjected to chronic immobilization stress at the whole genome level.

Methods: Rat whole genome expression chips (Illumina) were used to detect differences in hippocampal gene expression in rats from the control group (CN group), model group (M group) and Xiaoyaosan group (XYS group) that were subjected to chronic immobilization stress. The Gene Ontology terms and signaling pathways that were altered in the hippocampus gene expression profile were analyzed. The network regulating the transcription of the differentially expressed genes was also established. To verify the results from the gene chips, real-time quantitative polymerase chain reaction was used to determine the expression of the GABRA1, FADD, CRHR2, and CDK6 genes in hippocampal tissues. In situ hybridization (ISH) and immunohistochemistry were used to determine the expression of the GABRA1 and CRHR2 genes and proteins, respectively.

Results: Compared with the CN group, 566 differentially expressed genes were identified in the M group. Compared with the M group, 544 differentially expressed genes were identified in the XYS group. In the M and XYS groups, multiple significantly upregulated or downregulated genes functioned in various biological processes. The cytokine receptor interaction pathway was significantly inhibited in the hippocampus of the model group. The actin cytoskeleton regulation pathway was significantly increased in the hippocampus of the XYS group. The inhibition of hippocampal cell growth was the core molecular event of network regulating the transcription of the differentially expressed genes in the model group. Promotion of the regeneration of hippocampal neurons was the core molecular event of the transcriptional regulatory network in the XYS group. The levels of the GABRA1, FADD, CRHR2 and CDK6 mRNAs, and proteins were basically consistent with the results obtained from the gene chip.

Conclusion: XYS may have the ability of resistance to stress, enhancement immunity and promotion nerve cell regeneration by regulating the expression of multiple genes in numerous pathways and repaired the stress-induced impairments in hippocampal structure and function by inducing cytoskeletal reorganization. These results may provide the possible target spots in the treatment of stress in rats with XYS.

Author(s) Details:

Xiao-Hong Li,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China and School of Basic Medical Sciences, Guangxi University of Chinese Medicine, Nanning, China.

Xue-Ming Zhou,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China and School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, China.

Xiao-Juan Li,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China and Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou-510632, Guangdong, China.

Yue-Yun Liu,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China.

Qun Liu,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China.

Xiao-Ling Guo,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China.

Li-Qiang Yang,
School of Basic Medical Sciences, Guangxi University of Chinese Medicine, Nanning, China.

Jiaxu Chen,
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 11 North Third Ring Road Chaoyang District, Beijing-100029, China and Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou-510632, Guangdong, China.

Please see the link here: https://stm.bookpi.org/IPTDFTPX/article/view/11268

Factors Influencing Information Source Utilization among Maize Farmers in Edo State, Nigeria| Chapter 12 | Current Approaches in Science and Technology Research Vol. 1

 The factors of information source utilization restrictions among maize farmers in Nigeria's Edo state were investigated in this study. In agriculture, the importance of knowledge in advancing agricultural progress cannot be overstated. Increased agricultural production and improved marketing and distribution techniques require more information. With the help of a standardized questionnaire approved by expert judgment, data was collected from 150 randomly selected maize farmers and analyzed using frequency, percentage, mean, and Chi-square for hypothesis testing. The majority of respondents (66.0 percent), (59.3 percent), (53.3 percent), and (50.7 percent) had farms of less than one hectare, were married, and were males and household heads, respectively. Radio was the most popular source of information. (M = 2.60), posters and billboards (M = 2.59), and books and leaflets (M = 2.58). Poor radio and television signals (M = 3.42), insufficient rural electrification and constant power outages (M = 3.23), agriculture information and radio aired at odd hours (M = 3.15), and a lack of time to listen to agriculture were the most serious constraints faced in the use of information sources. Agriculture information (M = 3.10) is not carried on radio or television in my area (M = 2.99). At the 0.05 level of significance, there was a significant association between respondents' access to credit (R2 = 7.416), farm ownership (R2 = 12.497), sex (X2 = 13.759), and limits experienced in information source usage. It was suggested that the Ministry of Agriculture study time for broadcasting agriculture information in understandable language in order to attract maize farmers to listen. Radio and television signals need to reach rural areas in order to improve farmer audience levels for increased use of proven technologies information.

Author (s) Details

D. U. Okoedo-Okojie
Department of Agricultural Economics and Extension Services, Faculty of Agriculture, University of Benin, Benin City, Nigeria.

View Book :- https://stm.bookpi.org/CASTR-V1/article/view/1344