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The objective of this study is to enhance the precision of AI virtual systems by implementing Novel Hand Gesture Recognition techniques in comparison to Convolutional Neural Network. Materials and Methods: To recognise hand motions, a Convolutional Neural Network with distinct training and testing stages is utilized. The average Gpower for the test is between 0.05 and 0.85, or around 85%. Sample size is determined as 27,455 for each group using G Power 3.1 software (G Power setting parameters: α=0.05 and power=0.85). Results and Discussion: Novel Hand gesture recognition 92.60% identifies between objects and boosts the observed accuracy with a statistically non-significant value of p=0.123 (p>0.05) in comparison to the convolutional neural network's 88.59%. Conclusion: Comparison of the Novel Hand gesture Recognition algorithm and Convolutional Neural Network in terms of performance that shows Hand gesture recognition has 91.62% with better accuracy.

In Tamil Nadu, rice is the predominant crop during samba season. In the Cauvery Delta Zone around 2.5 lakh hectares purely depend on water from Cauvery in Samba season during the normal years of water release from Cauvery, CR 1009 is predominantly grown variety without any alternate varieties. Mono cropping of CR 1009 leads to susceptibility to pests such as brown plant hoppers (BPH) stem borers and diseases such as blast, bacterial leaf blight (BLB), grain discolouration and false smut. In order to provide a choice variety to farmers’ one long duration rice culture, AD 09367 a derivative of the cross BPT 5204 / Improved White ponni was released as Rice ADT 51 during the year 2018. ADT 51 matures in 155 to 160 days with tall, erect and high tillering plant habit and well adapted to single season canal dependent rice growing areas of Cauvery delta for Samba season (August-September sowing). ADT 51 has recorded a mean grain yield of 6533 kg/ha which is 9.8 and 12.7 per cent higher than CR 1009 (5947 kg/ha) and ADT 50 (5796 kg/ha) respectively. At Thiruvisanallur, Thanjavur district it manifested highest yield of 10036 kg/ha and the OFT farmer, Mr. Baskar received first prize in the district level crop yield competition for the year 2016. Besides its yield superiority, ADT 51 is resistant to blast and moderately resistant to leaf folder, stem borer, BPH, Sheath blight and sheath rot. The milled rice of this variety is white, medium grade with a 1000 grain weight of 23.9 g. High milling and head rice yield and cooking qualities of ADT 51 are comparable with CR 1009. Opinion from farmers revealed that, vigorous growing nature and tolerance to major pest and diseases of this culture requires less inputs especially nitrogenous fertilizer and plant protection chemicals for cultivation. The characters such as more no. of productive tillers, less chaffiness and quick establishment of seedling are its added advantages.

The intrusion detection system (IDS) plays an imperative role in defending the network from attacks. But, the IDS data is imbalanced, making the process complex for detecting the attacks accurately. According to these problems, this study proposes a network intrusion detection system based on an enhanced synthetic minority oversampling technique (SMOTE) and lightweight hybrid CAE (convolutional auto encoder)-ELM (extreme learning machine) model. Initially, the normalization of the original data is performed to avoid the influence of the maximum and minimum values. Secondly, an enhanced SMOTE is employed for solving the issues of the less detection rate of minority-attacks because of less training data. Here, the war strategy optimization (WSO) is incorporated with the SMOTE to design a novel WSO-based SMOTE technique for balancing the dataset. The three major steps utilized in WSO-based SMOTE are WSO based clustering, filtering and over-sampling. Then, the Information gain and fisher score based features extraction is employed for the reduction of computational complexity prior to the intrusion detection. Finally, the lightweight hybrid CAE-ELM model is executed for attack detection. CAEs are one of the most commonly used learning models because of their ability to construct a higher-level feature representation from the input data. Another model used in intrusion detection is the ELM, which provides an acceptable discrimination performance as well as a fast speed of learning. The performance of the proposed NIDS model is tested on the two benchmark datasets and achieved better accuracies of 99.21% and 99.15% on the UNSW-NB15 and CSE-CIC-IDS2018 datasets. [ABSTRACT FROM AUTHOR]

Human activities have had a significant impact on marine life and ecosystems, necessitating the implementation of conservation strategies to mitigate further damage. This abstract examines various conservation strategies and their effectiveness in addressing the human impact on marine environments. Industrialization, overfishing, pollution, and climate change have all had serious consequences for marine life. Coral reefs, seagrass meadows, and marine biodiversity have been degraded to unprecedented levels. To protect marine ecosystems, conservation strategies such as marine protected areas (MPAs), sustainable fishing practises, and pollution control measures have been implemented. MPAs have emerged as powerful tools for safeguarding critical habitats and species. They increase biodiversity, improve fish stocks, and mitigate the effects of human activity. Their success, however, is dependent on proper design, enforcement, and long-term monitoring. Sustainable fishing practices, such as ecosystem-based management and the implementation of fishing quotas, aim to ensure the long-term viability of fish populations while lowering bycatch and habitat destruction. These strategies have shown promise in restoring fish stocks and maintaining ecosystem health. Pollution control measures, such as wastewater treatment and stricter regulations on industrial and shipping activities, have been critical in reducing marine pollution. However, more comprehensive approaches are required to address emerging threats such as microplastics and chemical contaminants. The study's primary goal is to identify the factors that influence the organic product market in India. A sample size of 201 is used for empirical research. Chennai, Tamil Nadu, is the sample location. Age, gender, marital status, educational level, occupation, and monthly income are the independent variables. Biological Data, Environmental Data and Socio-economic Data are the dependent variables. It is possible to conclude that there is a lack of a well-established market for organic products in India, despite the fact that organic products have a high export potential. Finally, conservation strategies are critical for mitigating human impacts on marine life and ecosystems. MPAs, sustainable fishing practises, and pollution control measures can all help restore and protect marine biodiversity. However, ongoing research, adaptive management, and international cooperation are required to develop and refine these strategies to address emerging challenges in marine conservation. [ABSTRACT FROM AUTHOR]

This research study aims to examine the socioeconomic and occupational status of the fisherman community in the Thoothukudi district, located in the southern part of Tamil Nadu, India. The fishing community plays a vital role in the region's economy, contributing to the livelihoods of numerous households and supporting the local fishery industry. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data on various aspects of the fisherman community's socioeconomic conditions and occupational dynamics. A representative sample of fishermen and their families residing in different coastal villages within the Thoothukudi district will be selected for data collection. The quantitative phase involves administering structured surveys to collect data on demographic characteristics, education levels, household income, asset ownership, and access to social welfare programs. Additionally, information related to occupational factors such as fishing techniques, equipment, fishing practices, and income generated from fishing activities will be collected. The qualitative phase comprises in-depth interviews with a subset of participants to explore their perceptions, challenges, and aspirations related to their occupation and socio-economic circumstances. The qualitative data will provide a nuanced understanding of the fisherman community's lived experiences, social networks, and community support systems. The collected data will be analyzed using appropriate statistical methods and qualitative thematic analysis techniques. The findings of this research are expected to shed light on the current socioeconomic and occupational status of the fisherman community in the Thoothukudi district. The study will also identify key challenges and opportunities faced by the community, including issues related to poverty, education, healthcare, and infrastructure. The results of this study can inform policymakers, government agencies, and non-governmental organizations working towards the welfare and development of the fisherman community. By understanding the socio-economic and occupational dynamics, stakeholders can design targeted interventions and policies to enhance the livelihoods, wellbeing, and sustainable development of the fisherman community in the Thoothukudi district and similar coastal regions. [ABSTRACT FROM AUTHOR]