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With artificial intelligence technology progressing at a tremendous speed, intelligent driving has got a lot of recognition in recent years. Lane detection is one of the primary functions in self-driving cars. Traditionally, lane detection was done using image processing algorithms and computer vision techniques, which included extraction of areas which are possible lane areas, edge enhancement etc. Deep learning models with new improvements are being introduced till date. Additionally, a self-driving vehicle must be able to recognise traffic signs. In the proposed work a VGG-16 convolutional neural network is used for road segmentation. The model is trained on the KITTI road/lane detection evaluation 2013 dataset. The model performed well with an accuracy of 98.58%. For traffic sign detection, the German traffic sign recognition benchmark dataset is used. A convolutional neural network is used with ADAM optimizer, which gives an accuracy of 95%. [ABSTRACT FROM AUTHOR]

Green and sustainable synthesis of carbon dots (CD) has garnered significant interest due to their versatile applications and environmentally friendly production methods. Its primary characteristics of demonstrating superior optical properties, like high quantum yields and lengthy photoluminescence decay lifetimes, cheaper methods of preparation due to readily available raw materials and simpler preparation techniques, biocompatibility, and tunable structural functionalization have made it the material preferred in many contemporary research fields. In this study, we represent a novel approach for the synthesis of CD using date seed extract as a natural spring of carbon. The CD were fabricated via a simplistic one-pot hydrothermal method, avoiding the use of toxic chemicals and energy-intensive processes. Characterization techniques, including transmission electron microscopy (TEM), Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, UV-visible spectroscopy, and photoluminescence spectroscopy, were employed to analyze the properties of the synthesized CD. The analysis using TEM revealed, that the CD exhibited a spherical morphology with an regular size of [20 up to 40 nm]. The Raman spectroscopy confirmed the presence of sp2 carbon, indicating their graphene-like structure. FTIR analysis provided insights into the functional groups existing on the surface of the CD. The UV-visible spectroscopy demonstrated strong absorbance in the visible region, indicating their potential for photoluminescence applications. The photoluminescence spectroscopy further revealed their tunable emission properties. Utilizing a green process to create CD extract offers a sustainable and renewable pathway for the production of nanomaterials, promoting environmental responsibility. [ABSTRACT FROM AUTHOR]

The common insecticide used in Cabbage and Basmati rice fields is Cartap hydrochloride (CHC). It is a low toxicity insecticide belongs to Neurotoxin. Present study aimed to synthesize a new Nano-insecticide by using polypropylene glycol (PPG) as an encapsulated agent. Nano-CHC is useful for plant disease control. Polypropylene glycol encapsulated CHC size distribution was obtained by PadeLaplace dispersion in the size range in between 60-67nm. The functional group analysis of encapsulated CHC was carried by FT-IR spectroscopy. The structural topology of encapsulated CHC was obtained by scanning electron microscope. The morphological internal structural elucidation of encapsulated CHC can be obtained by transmission electron microscope analysis. The bioactivity study was conducted and shows good efficacy results against Aspergillus flavus, Sclerotium rolfsii and Rhizoctonia solani. [ABSTRACT FROM AUTHOR]

The occurrence of botnets over the network is crucial as it shows advent effect on various applications like finance, cyber-security, and healthcare application. Botnets are refined and more dangerous in their functionality over the network model. Most of the prevailing models and flow- and rule-based models feel challenging to predict the bot functionalities in a preventive manner. Therefore, the modeling of efficient and automated botnet detection approaches is highly essential. This research concentrates on modeling a novel botnet detection approach based on the recursively analyzing the flow of features of the network nodes spatially and temporally where the attack samples are intra-dependent time-series data. The hierarchical structural design of the network helps to integrate various levels of feature information and learns the spatial and temporal information automatically among the adjacent network connection. This process is carried out by the proposed architectural model known as Recursively Learning Long Short-Term Memory over spatial and temporal ( RL 2 TM-st). Thus, the bot activities are detected by recursively analyzing the limited number of nodes. The RL 2 TM-st) model is modeled to improve the efficiency of the network by eliminating unnecessary activities. The proposed model is validated using the online accessible CTU-13 dataset and benchmarked against the prevailing classification approaches for botnet detection. The simulation is done in a MATLAB environment, and the outcomes work efficiently and evaluated with prevailing models to project the significance of the RL 2 TM-st model. [ABSTRACT FROM AUTHOR]

Bot detection is considered a crucial security issue that is extensively analysed in various existing approaches. Machine Learning is an efficient way of botnet attack detection. Bot detection is the major issue faced by the existing system. This research concentrates on adopting a graph-based feature learning process to reduce feature dimensionality. The incoming samples are correctly classified and optimised using an Adaboost classifier with an improved grey wolf optimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multi-constraint issues related to bot detection and provide better local and global solutions (to satisfy exploration and exploitation). The extensive results show that the proposed g-AGWO model outperforms existing approaches to reduce feature dimensionality, under-fitting/over-fitting and execution time. The error rate prediction shows the feasibility of the given model to work over the challenging environment. This model also works efficiently towards the unseen data to achieve better generalization. [ABSTRACT FROM AUTHOR]

The current agricultural practice is to develop novel Nano agrochemicals in the form of Nano pesticides. Many researchers in the world have foretold an upheaval in present agricultural practice. This paper aims at the application of nanotechnology for plant protection in the form of Nano pesticides. Our study endowed with a novel polymeric Nano formulation of a dithiocarbamate nonsystemic agricultural fungicide called Mannose (MCB) by using polycaprolactone as a capping agent. The encapsulated dithiocarbamate complex was characterized by using analytical techniques like UV-Visible spectroscopy, Dynamic light scattering, Scanning electron microscope, and Transmission electron microscope. The particle size distribution emerged at 17-44nm. The bioassay was conducted against Aspergillus flavus, Aspergillus niger, Sclerotium rolfsii, and Rhizoctonia solani. The bioassay illustrated better results as compared to the commercial pesticide. [ABSTRACT FROM AUTHOR]