Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in accordance with their respective needs. The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion, the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process

Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in accordance with their respective needs. The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion, the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process

Antitumor activity of silver nanoparticles in Dalton’s lymphoma ascites tumor model

Muthu Irulappan Sriram, Selvaraj Barath Mani Kanth, Kalimuthu Kalishwaralal, Sangiliyandi GurunathanDepartment of Biotechnology, Division of Molecular and Cellular Biology, Kalasalingam University, Tamilnadu, IndiaAbstract: Nanomedicine concerns the use of precision-engineered nanomaterials to develop novel therapeutic and diagnostic modalities for human use. The present study demonstrates the efficacy of biologically synthesized silver nanoparticles (AgNPs) as an antitumor agent using Dalton’s lymphoma ascites (DLA) cell lines in vitro and in vivo. The AgNPs showed dose-dependent cytotoxicity against DLA cells through activation of the caspase 3 enzyme, leading to induction of apoptosis which was further confirmed through resulting nuclear fragmentation. Acute toxicity, ie, convulsions, hyperactivity and chronic toxicity such as increased body weight and abnormal hematologic parameters did not occur. AgNPs significantly increased the survival time in the tumor mouse model by about 50% in comparison with tumor controls. AgNPs also decreased the volume of ascitic fluid in tumor-bearing mice by 65%, thereby returning body weight to normal. Elevated white blood cell and platelet counts in ascitic fluid from the tumor-bearing mice were brought to near-normal range. Histopathologic analysis of ascitic fluid showed a reduction in DLA cell count in tumor-bearing mice treated with AgNPs. These findings confirm the antitumor properties of AgNPs, and suggest that they may be a cost-effective alternative in the treatment of cancer and angiogenesis-related disorders.Keywords: antitumor, silver nanoparticles, Dalton’s lymphoma, ascite

Self-Tunable, Exfoliated Oxygen-Rich Flower-like MoS₂ Nanosheets for Arsenic Removal: Investigations on Substitution, Stability, and Sustainability (3S) for Maxi-Sorption

In this study, we synthesized La-incorporated O-rich defective MoS2 nanosheets by a simple, inexpensive, in situ hydrothermal reaction to self-exfoliate the bulky MoS2 layers themselves so that they can readily trap hard base anions, arsenic (arsenite and arsenate), from water. Attempting to modify MoS2 surfaces by incorporating O allows for more active sites, which is confirmed by powder XRD patterns where the exfoliated layers have a d-spacing of 0.63 nm, while the spacing for the bulky layers is 0.60 nm. The substitution of La at different equivalent ratios on the interlayer/surface improves the adsorption properties of arsenite and arsenate in simple solutions, as shown by the Langmuir adsorption density values of 0.7760 and 1.4363 mmol g–1, respectively. When the O-rich MoS2 layers were loaded with La, the adsorption densities improved, with La1.0 equiv showing the best values among the materials studied. The presence of O and S was more responsible for the removal of arsenite ions, and La and O, together with a small amount of N, were able to remove arsenate ions from water according to the well-known Pearson’s Lewis acid−base principle. The stability of the materials was characterized after the experiments, and it was found that there was no leaching of the materials by ICP-OES and the stability was maintained after 6 regeneration cycles. With the exception of phosphate, which behaves chemically similar to arsenic, the adsorption densities were not significantly affected by the mono- and divalent anions, indicating the selectivity of the prepared materials. The synthesis cost of MoOxS2–x was 2 times lower than that of bulky MoS2, and its adsorption properties were 10 times higher than those of the latter. The results suggest that La-substituted O-rich MoS2 is a potential candidate for the removal of soft and hard base metals from water