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Agriculture is crucial to India’s rural economy, with vegetable cultivation being a key component. This study focuses on 150 rural farmers in Assam’s Dibrugarh District, exploring their socioeconomic characteristics, marketing practices, factors influencing these practices and the barriers they experience. The rapid sale of vegetables, driven by their perishable nature, emerged as the most influential factor in farmers’ marketing practices. It was found that farmers face various barriers like perishability of vegetables, limited access to ICT tools and low level of knowledge. Findings revealed a significant association between socioeconomic characteristics and marketing practices.

Adoption of farm innovations in food crops is necessary to modernize agricultural practices, improve efficiency and increase yields to meet the growing global food demand. This study analysed the adoption trends of innovations in paddy cultivation. Key innovations were identified including the adoption of advanced agricultural machinery starting from seed preparation to harvesting and storage, improved irrigation systems and utilization of hybrid rice varieties by interviewing the paddy farmers of Assam, India. Percentage analysis and paired t-test were used to assess the impact of innovations on rice crop productivity. The results of the study indicate a positive and progressive trend in the adoption of innovations in paddy cultivation. The widespread acceptance of improved machineries and a diverse array of hybrid rice varieties showcases a collective effort among farmers to enhance productivity, optimize resource utilization, and embrace sustainable agricultural practices. To maximize the benefits of these innovations, farmers must receive adequate training.

Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.

The study was conducted to assess the e-connectivity infrastructure available in the Gram Panchayat (Village council) and analyze the software applications adopted by the Gram Panchayats. The study area was three blocks of Coimbatore district, Tamil Nadu viz., Karamadai, Periyanaickenpalayam and Thondamuthur. A total of 36 Gram Panchayat presidents formed the sample. The study revealed that all the Gram Panchayats from three blocks were well equipped with e-connectivity, Because of this the respondents were able to adopt the software applications for reducing paper work in the Gram Panchayat.

Image processing with the help of machine learning algorithms is breaking barriers in various fields of study, especially in the medical field. Deep learning image classification algorithms have made disease detection based on images in an easy manner thus assists the medical professional to taking quick decisions. This paper discusses various deep learning algorithms and transfer learning methods used to classify various eye diseases. Kaggle Eye dataset is used for this purpose. We compared four deep learning algorithms, namely EfficientNetB3, Inception V3, VGG 19 and Convolutional Neural Network models. Various categories of eye diseases, namely Cataract, Diabetic Retinopathy, Glaucoma are considered for classification with normal eye with the help of the scanned images. The strengths and weaknesses of these models are compared based on Precision, Recall, Accuracy and F1 score. In an identical testing environment, EfficientNet B3 outperforms the other algorithms and provides better accuracy for the classification of eye diseases. [ABSTRACT FROM AUTHOR]