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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]

Marine losses are immense that they affect not only the Insurance Companies, but the whole of economy and trade to a great extent. It is a fact that the safety regulations are instrumental in minimising the loss experience and is not an assurance for loss avoidance. But the growth in cargo values and technological developments in ship makes the loss minimisation measures paramount for marine underwriters. Hence, it is essential to continuously examine the major causes of marine losses and propose risk management measures to mitigate the loss. Exceptional forethought is needed for risk management strategies to establish sustainable and resilient Maritime Industry. Technological innovations are the primary contributors to respond to maritime threats. Tracking and monitoring technologies bring improved control and visibility throughout the supply chain. The modern Marine Insurance industry is viewing digital disruption as opportunity to enhance better operations and profitability. A thorough understanding of maritime threat and losses is essential for insurers to effectively manage risk, price policies accurately and maintain a stable and competitive marine insurance market. [ABSTRACT FROM AUTHOR]

Forest fires are a global natural calamity causing significant economic damage and loss of lives. Professionals forecast that forest fires would raise in the future because of climate change. Early prediction and identification of fire spread would enhance firefighting and reduce affected zones. Several systems have been advanced to detect fire. Recently, Unmanned Aerial Vehicles (UAVs) can be used for forest fire detection due to their ability, high flexibility, and inexpensive to cover vast areas. But still, they are limited by difficulties like image degradation, small fire size, and background complexity. This study develops an automated Forest Fire Detection using Metaheuristics with Deep Learning (FFDMDL-DI) model. The presented FFDMDL-DI technique exploits the DL concepts on drone images to identify the occurrence of fire. To accomplish this, the FFDMDL-DI technique makes use of the Capsule Network (CapNet) model for feature extraction purposes with a biogeography-based optimization (BBO) algorithm-based hyperparameter optimizer. For accurate forest fire detection, the FFDMDL-DI technique uses a unified deep neural network (DNN) model. Finally, the tree growth optimization (TGO) technique is utilized for the parameter adjustment of the DNN method. To depict the enhanced detection efficiency of the FFDMDL-DI approach, a series of simulations were performed on the FLAME dataset, comprising 6000 samples. The experimental results stated the improvements in the FFDMDL-DI method over other DL models with maximum accuracy of 99.76%. [ABSTRACT FROM AUTHOR]

Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC model, the synergic deep learning (SDL) model is exploited to produce feature vectors. The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs. The ISMO algorithm is used to enhance the classification efficiency of the GCN method, which is derived by integrating chaotic concepts into the SMO algorithm. The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset. [ABSTRACT FROM AUTHOR]

Neuroeducation, an emerging interdisciplinary field at the intersection of neuroscience and education, investigates into the complex processes underpinning learning which offering novel insights and methodologies. This article examines the foundational principles of neuroeducation, descriptive dynamic interplay between neuronal mechanisms and pedagogical strategies. Central to this exploration is an appreciation for the brain's remarkable plasticity and adaptability, highlighting how learning experiences sculpt neuronal networks. Utilizing advanced imaging techniques and rigorous neuroscientific research, a wealth of insights has emerged, enhancing our understanding of cognition, memory formation, and information processing. These insights enrich our comprehension of individual learning mechanisms and guide the development of effective educational interventions. Grounded in interdisciplinary collaboration, neuroeducation seamlessly integrates perspectives from neuroscience, psychology, and education to develop instructional approaches aligned with the brain's intrinsic learning processes. This collaboration nurtures, innovation in teaching methodologies, enhances student engagement, and bolsters academic achievement. Furthermore, the integration of technology into educational practices, ranging from brain-computer interfaces to immersive virtual reality experiences, presents new possibilities for enhancing learning engagements and accommodating diverse learning styles and curricula. Through the strategic use of technology's transformative power, educators can create immersive learning environments that stimulate cognitive processes and enhance knowledge retention. Neuroeducation emerges as a transformative force in educational theory and practice, empowering educators to design pedagogical approaches finely attuned to the complexities of the human brain, this paves the way for a more inclusive and effective educational landscape. This study is nested within a systematic review of the past 15 years of digital depository research and models of neuroeducational discourses, underscoring the field's evolving nature and its impact on contemporary education.