Background: MANET is a self-organized wireless network with no infrastructure. Especially data transfer from one system to another system needs to be done in a secure way. In order to provide data integrity, authentication plays an important role in data communication. RSA and ECC are widely used algorithms in the real world, but authentication using these algorithms is time-consuming. Towards this, various algorithms came into existence with different security primitives. However, it is important to design an effective key agreement process with reduced computational cost among these security mechanisms. We have designed an effective mechanism to mitigate packet dropping attacks to secure end-to-end communication in MANET. Objective: The proposed light weight authentication method is based on chaotic maps that uses Chebyshev polynomials as primitive operation. Methods: The methodology includes semi group property of Chebyshev polynomials and also the discrete logarithmic problem based chaotic maps that takes less time than these existing algorithms and evaluates with respect to attack Resilience, packet deliverya fraction, delay, throughput, and overhead. Results: Simulation produces the result of the proposed mechanism and give better performance in terms of packet delivery ratio, overhead and computational cost. Conclusion: Authentication based on developed mechanism consumes less time as shown in statistical analysis and mitigates packet dropping attacks effectively than that of traditional methods like RSA and ECC with respect to key generation mechanism.

Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.

The burden of neurological illnesses on global health is significant. Our perception of the molecular and biological mechanisms underlying intellectual processing and behavior has significantly advanced over the last few decades, laying the groundwork for potential therapies for various neurodegenerative diseases. A growing body of literature reveals that most neurodegenerative diseases could be due to the gradual failure of neurons in the brain's neocortex, hippocampus, and various subcortical areas. Research on various experimental models has uncovered several gene components to understand the pathogenesis of neurodegenerative disorders. One among them is the brain-derived neurotrophic factor (BDNF), which performs several vital functions, enhancing synaptic plasticity and assisting in the emergence of long-term thoughts. The pathophysiology of some neurodegenerative diseases, including Alzheimer's, Parkinson's, Schizophrenia, and Huntington's, has been linked to BDNF. According to numerous research, high levels of BDNF are connected to a lower risk of developing a neurodegenerative disease. As a result, we want to concentrate on BDNF in this article and outline its protective role against neurological disorders.

Cancer is a complex and debilitating disease that is one of the leading causes of death in the modern world. Computational methods have contributed to the successful design and development of several drugs. The recent advances in computational methodology, coupled with the avalanche of data being acquired through high throughput genomics, proteomics, and metabolomics, are likely to increase the contribution of computational methods toward the development of more effective treatments for cancer. Recent advances in the application of neural networks for the prediction of the native conformation of proteins have provided structural information regarding the complete human proteome. In addition, advances in machine learning and network pharmacology have provided novel methods for target identification and for the utilization of biological, pharmacological, and clinical databases for the design and development of drugs. This is a review of the key advances in computational methods that have the potential for application in the design and development of drugs for cancer.

Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.

Retinal fundus imaging has been used in the diagnosis of many cardiovascular and retinal-related diseases like age-related macular degeneration, pathological myopia, diabetic retinopathy, and glaucoma. In recent years, the computerized technique is adapted to fundus image analysis and processing for quick diagnosis. However, low-quality retinal pictures are produced when certain eye disorders and photographic circumstances exist. Poor-quality retinal pictures of this kind are not helpful for diagnosis, particularly when using automated image analysis software. The causes for degradation include low illumination, high illumination, image blurring, uneven illumination, low contrast, and color distortion. Several image-enhancing techniques have been developed to address this; however, they may result in sudden changes in color levels, false borders, and the loss of picture detail, particularly in retinal images. To prevent these negative consequences, this paper proposed a novel enhancement algorithm EGF_AGC (Enhanced Guided Filter+Adaptive Gamma Correction) for color fundus retinal images. The proposed method is worked is described in four steps. The first stage of the recommended procedure is to utilize a hybrid filter to enhance the retinal pictures’ appearance details. Next, applied enhanced guided filter method to the detailed retina images to enhance the image quality. Moreover, the suggested technique also worked to retain uneven illumination issues raised in retina images using the Adaptive Gamma Correction method. The final improved retinal pictures are created by combining the phases before utilizing the HSV color approach. The databases of retinal images, which include the STARE, the DRIVE, and the CHBASE_DB1 database, were utilized to determine image enhancement effects. The findings were compared with CLAHE, Bilateral Filter, Guided Filter, and Gamma Correction retinal enhancement methods. Compared to similar enhancement approaches, experiments showed that our method could give a competitive outcome and eliminate degradation caused by low-quality fundus pictures.

This study investigates the impact of effectuation and co-creation on the success of social enterprises in India. Given the hybridisation in their operations, social enterprises operate under highly uncertain environments. Although scholars have attempted to identify approaches and strategies for scaling social enterprises, we propose the relevance of effectuation and co-creation in social entrepreneurial action to drive social enterprises in uncertain environments. To achieve this, we examined government support (GS) and slack resources (SR) as environmental and firm-level factors respectively in impacting the performance of social enterprises. We gathered empirical data from a sample of 181 social enterprises based in India and utilised hierarchical regression in our analytical approach. The results largely support our research model.

Green synthesis of metal nanoparticles is an attractive substitute for traditional methods using capping and reducing chemicals. In this study, silver nanoparticles (AgNPs) were synthesised using carbon dots (CDs) derived from bioresources as reducing, protecting, and stabilising agents in a single step using environmentally friendly and cost-effective synthetic methods. The optical and structural properties of prepared CD/AgNPs were explored using UV–vis (Ultraviolet-Visible Spectroscopy), Fluorescence spectroscopy, XRD (x-ray Diffraction), DLS (Dynamic Light Scattering), SEM-EDX (Scanning Electron Microscopy with Energy-Dispersive x-ray Spectroscopy) and TEM (Transmission Electron Microscopy). The synthesised CD/AgNPs are stable as zeta potential value is −14.7mV. From TEM the particle size exhibited as ∼12 nm. The prepared CD/AgNPs exhibited significant optical absorbance, good water dispersibility, stability and nano size. Also, CD/AgNPs revealed good biocidal effects against Gram-negative bacteria Escherichia coli (E. coli), Pseudomonas Aeruginosa (P. aeruginosa), Gram-positive Staphylococcus Aureus (S. aureus), Bacillus Cereus (B. cereus), and good anti-fungal activity against Aspergillus Niger (A. niger). The CD/AgNPs were further analyzed by live/dead assay. E. coli and A. niger with zone of inhibition around 3.1 and 40 mm, respectively when compared to ciprofloxacin (2.2 mm) and fluconazole (25 mm). The above investigation proved that the developed CD/AgNPs will be a new platform as an alternative to the traditional antibiotics for the generation of new kind of antibacterial materials and also provide the pathway for various metal/CD nanomaterials for diverse biomedical applications.