In the recent times, there has been a lot of speculation related to advanced driver‐assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R‐CNN, Fast R‐CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real‐Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state‐of‐the‐art object detection algorithm YOLOv5 which improves the speed in detection of object over real‐time. This paper provides a comparison between other state‐of‐the‐art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “ObjectDetect” consists of a simple user interface that displays alerts and warnings.

In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.

Water pollution is a serious concern for developing and undeveloped countries. Photocatalytic degradation of organic pollutants is an effective degradation method to restrain the green ecosystem. This research article presents a green, low-cost, and benevolent eco-friendly biosynthesis of cobalt oxide (Co₃O₄) nanoparticles using Curcuma longa plant extract. The UV and visible region absorbance of Co₃O₄ nanoparticles estimated the Co²⁺ and Co³⁺ transitions on the lattice oxygen, and their bandgap of 2.2 eV was confirmed from the UV-DRS spectroscopy. The cubic structure and spherical shape of Co₃O₄ nanoparticles were estimated by using XRD and TEM characterizations. Plant molecules aggregation and their agglomerations on the nanoparticles were established from FTIR and EDX spectroscopy. Multiple cobalt valences on the oxygen surfaces and their reaction, bonding, and binding energies were analyzed from XPS measurements. The biogenic Co₃O₄ nanoparticles were executed against gram-positive (Staphylococcus aureus—S. aureus) and gram-negative (Escherichia coli—E. coli) bacteria. A gram-positive bacterial strain exhibited great resistivity on Co₃O₄ nanoparticles. Degradation of organic dye pollutants on the Co₃O₄ nanoparticles was performed against methylene blue (MB) dye under the conditions of visible light irradiation. Dye degradation efficiency pseudo-first-order kinetics on the pseudo-first-order kinetics denotes the rate of degradation over the MB dye. This research work achieved enhanced degradation potency against toxic organic dye and their radicals are excited from visible light irradiations. Absorption light and charged particle recombinations are reformed and provoked by the plant extract bio-molecules. In this process, there is no inferior yield development, and electrons are robustly stimulated. Furthermore, the biosynthesized Co₃O₄ nanoparticles determined the potency of bacterial susceptibility and catalytic efficacy over the industrial dye pollutants.

The green synthesis of metal oxide nanoparticles is an efficient, simple, and chemical-free method of producing nanoparticles. The present work reports the synthesis of Murraya koenigii-mediated ZrO₂ nanoparticles (ZrO₂ NPs) and their applications as a photocatalyst and antibacterial agent. Capping and stabilization of metal oxide nanoparticles were achieved by using Murraya koenigii leaf extract. The optical, structural, and morphological valance of the ZrO₂ NPs were characterized using UV-DRS, FTIR, XRD, and FESEM with EDX, TEM, and XPS. An XRD analysis determined that ZrO₂ NPs have a monoclinic structure and a crystallite size of 24 nm. TEM and FESEM morphological images confirm the spherical nature of ZrO₂ NPs, and their distributions on surfaces show lower agglomerations. ZrO₂ NPs showed high optical absorbance in the UV region and a wide bandgap indicating surface oxygen vacancies and charge carriers. The presence of Zr and O elements and their O=Zr=O bonds was categorized using EDX and FTIR spectroscopy. The plant molecules’ interface, bonding, binding energy, and their existence on the surface of ZrO2 NPs were established from XPS analysis. The photocatalytic degradation of methylene blue using ZrO₂ NPs was examined under visible light irradiation. The 94% degradation of toxic MB dye was achieved within 20 min. The antibacterial inhibition of ZrO₂ NPs was tested against S. aureus and E. coli pathogens. Applications of bio-synthesized ZrO₂ NPs including organic substance removal, pathogenic inhibitor development, catalysis, optical, and biomedical development were explored.

Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor’s hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved. Following its success in Natural language processing (NLP), the transformer paradigm has been adopted by the computer vision (CV) community of the NLP. While there are already accepted approaches to classifying the liver, especially in clinical settings, there is room for advancement in terms of their precision. This paper makes an effort to apply a novel model for segmenting and classifying liver tumours built on deep learning. In order to accomplish this, the created model follows a three-stage procedure consisting of (a) pre-processing, (b) liver segmentation, and (c) classification. In the first phase, the collected Computed Tomography (CT) images undergo three stages of pre-processing, including contrast improvement via histogram equalization and noise reduction via the median filter. Next, an enhanced mask region-based convolutional neural networks (Mask R-CNN) model is used to separate the liver from the CT abdominal image. To prevent overfitting, the segmented picture is fed onto an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet). The experimental results prove the superior performance of the proposed perfect on a wide variety of CT images, as well as its efficiency and low sensitivity to noise.

Recently, the preparation of smart multifunctional hybrid nanoparticles has captured significant interest in versatile areas, including medicine, environment, and food, due to their enhanced physicochemical properties. The present study focuses on the synthesis of Fe-doped NiO nanoparticles by the coprecipitation method using the sources of nickel (II) acetate tetrahydrate and iron (III) nitrate nonahydrate. The prepared Fe-doped NiO nanoparticles are characterized by X-ray diffraction, Fourier transform infrared spectroscopy, UV–visible spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, and X-ray photon spectroscopic analysis. The XRD results clearly confirm the face-centered cubic structure and polycrystalline nature of the synthesized Fe-NiO nanoparticles. The Tauc plot analysis revealed that the bandgap energy of the Fe-doped NiO nanoparticles decreased with the increasing concentration of the Fe dopant from 2% to 8%. The XPS analysis of the samples exhibited the existence of elements, including Fe, Ni, and O, with the absence of any surplus compounds. The FE-SEM and TEM analyses proved the formation of nanostructured Fe-NiO with few spherical and mostly unevenly shaped particles. Further, the photocatalytic efficiency of the prepared Fe-doped NiO nanoparticles were identified by using the cationic dye rhodamine B (Rh-B). The photocatalytic results proved the 8% of Fe doped with NiO nanoparticles achieved 99% of Rh-B degradation within 40 min of visible-light irradiation. Hence, the results of the present study exemplified the Fe-doped NiO nanoparticles have acted as a noticeable photocatalyst to degrade the Rh-B dye.